A searchable list of some of my publications is below. You can also access my publications from the following sites.
My ORCID is
https://orcid.org/0000-0002-6236-2969
Publications:
1.
Kumar, Niranjan, Essa, Irfan, Ha, Sehoon
Cascaded Compositional Residual Learning for Complex Interactive Behaviors Journal Article
In: IEEE Robotics and Automation Letters, vol. 8, iss. 8, pp. 4601–4608, 2023.
Abstract | Links | BibTeX | Tags: IEEE, reinforcement learning, robotics
@article{2023-Kumar-CCRLCIB,
title = {Cascaded Compositional Residual Learning for Complex Interactive Behaviors},
author = {Kumar, Niranjan and Essa, Irfan and Ha, Sehoon},
url = {https://ieeexplore.ieee.org/document/10152471},
doi = {10.1109/LRA.2023.3286171},
year = {2023},
date = {2023-06-14},
urldate = {2023-06-14},
journal = {IEEE Robotics and Automation Letters},
volume = {8},
issue = {8},
pages = {4601--4608},
abstract = {Real-world autonomous missions often require rich interaction with nearby objects, such as doors or switches, along with effective navigation. However, such complex behaviors are difficult to learn because they involve both high-level planning and low-level motor control. We present a novel framework, Cascaded Compositional Residual Learning (CCRL), which learns composite skills by recursively leveraging a library of previously learned control policies. Our framework combines multiple levels of pre-learned skills by using multiplicative skill composition and residual action learning. We also introduce a goal synthesis network and an observation selector to support combination of heterogeneous skills, each with its unique goals and observation space. Finally, we develop residual regularization for learning policies that solve a new task, while preserving the style of the motion enforced by the skill library. We show that our framework learns joint-level control policies for a diverse set of motor skills ranging from basic locomotion to complex interactive navigation, including navigating around obstacles, pushing objects, crawling under a table, pushing a door open with its leg, and holding it open while walking through it. The proposed CCRL framework leads to policies with consistent styles and lower joint torques, and successfully transfer to a real Unitree A1 robot without any additional fine-tuning.},
keywords = {IEEE, reinforcement learning, robotics},
pubstate = {published},
tppubtype = {article}
}
Real-world autonomous missions often require rich interaction with nearby objects, such as doors or switches, along with effective navigation. However, such complex behaviors are difficult to learn because they involve both high-level planning and low-level motor control. We present a novel framework, Cascaded Compositional Residual Learning (CCRL), which learns composite skills by recursively leveraging a library of previously learned control policies. Our framework combines multiple levels of pre-learned skills by using multiplicative skill composition and residual action learning. We also introduce a goal synthesis network and an observation selector to support combination of heterogeneous skills, each with its unique goals and observation space. Finally, we develop residual regularization for learning policies that solve a new task, while preserving the style of the motion enforced by the skill library. We show that our framework learns joint-level control policies for a diverse set of motor skills ranging from basic locomotion to complex interactive navigation, including navigating around obstacles, pushing objects, crawling under a table, pushing a door open with its leg, and holding it open while walking through it. The proposed CCRL framework leads to policies with consistent styles and lower joint torques, and successfully transfer to a real Unitree A1 robot without any additional fine-tuning.
2.
Dina Bashkirova, José Lezama, Kihyuk Sohn, Kate Saenko, Irfan Essa
MaskSketch: Unpaired Structure-guided Masked Image Generation Proceedings Article
In: IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), 2023.
Abstract | Links | BibTeX | Tags: computer vision, CVPR, generative AI, generative media, google
@inproceedings{2023-Bashkirova-MUSMIG,
title = {MaskSketch: Unpaired Structure-guided Masked Image Generation},
author = { Dina Bashkirova and José Lezama and Kihyuk Sohn and Kate Saenko and Irfan Essa},
url = {https://arxiv.org/abs/2302.05496
https://openaccess.thecvf.com/content/CVPR2023/papers/Bashkirova_MaskSketch_Unpaired_Structure-Guided_Masked_Image_Generation_CVPR_2023_paper.pdf
https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bashkirova_MaskSketch_Unpaired_Structure-Guided_CVPR_2023_supplemental.pdf},
doi = {10.48550/ARXIV.2302.05496},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
booktitle = {IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR)},
abstract = {Recent conditional image generation methods produce images of remarkable diversity, fidelity and realism. However, the majority of these methods allow conditioning only on labels or text prompts, which limits their level of control over the generation result. In this paper, we introduce MaskSketch, an image generation method that allows spatial conditioning of the generation result using a guiding sketch as an extra conditioning signal during sampling. MaskSketch utilizes a pre-trained masked generative transformer, requiring no model training or paired supervision, and works with input sketches of different levels of abstraction. We show that intermediate self-attention maps of a masked generative transformer encode important structural information of the input image, such as scene layout and object shape, and we propose a novel sampling method based on this observation to enable structure-guided generation. Our results show that MaskSketch achieves high image realism and fidelity to the guiding structure. Evaluated on standard benchmark datasets, MaskSketch outperforms state-of-the-art methods for sketch-to-image translation, as well as unpaired image-to-image translation approaches.},
keywords = {computer vision, CVPR, generative AI, generative media, google},
pubstate = {published},
tppubtype = {inproceedings}
}
Recent conditional image generation methods produce images of remarkable diversity, fidelity and realism. However, the majority of these methods allow conditioning only on labels or text prompts, which limits their level of control over the generation result. In this paper, we introduce MaskSketch, an image generation method that allows spatial conditioning of the generation result using a guiding sketch as an extra conditioning signal during sampling. MaskSketch utilizes a pre-trained masked generative transformer, requiring no model training or paired supervision, and works with input sketches of different levels of abstraction. We show that intermediate self-attention maps of a masked generative transformer encode important structural information of the input image, such as scene layout and object shape, and we propose a novel sampling method based on this observation to enable structure-guided generation. Our results show that MaskSketch achieves high image realism and fidelity to the guiding structure. Evaluated on standard benchmark datasets, MaskSketch outperforms state-of-the-art methods for sketch-to-image translation, as well as unpaired image-to-image translation approaches.
3.
Lijun Yu, Yong Cheng, Kihyuk Sohn, José Lezama, Han Zhang, Huiwen Chang, Alexander G. Hauptmann, Ming-Hsuan Yang, Yuan Hao, Irfan Essa, Lu Jiang
MAGVIT: Masked Generative Video Transformer Proceedings Article
In: IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), 2023.
Abstract | Links | BibTeX | Tags: computational video, computer vision, CVPR, generative AI, generative media, google
@inproceedings{2023-Yu-MMGVT,
title = {MAGVIT: Masked Generative Video Transformer},
author = {Lijun Yu and Yong Cheng and Kihyuk Sohn and José Lezama and Han Zhang and Huiwen Chang and Alexander G. Hauptmann and Ming-Hsuan Yang and Yuan Hao and Irfan Essa and Lu Jiang},
url = {https://arxiv.org/abs/2212.05199
https://magvit.cs.cmu.edu/
https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_MAGVIT_Masked_Generative_Video_Transformer_CVPR_2023_paper.pdf
https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_MAGVIT_Masked_Generative_CVPR_2023_supplemental.pdf},
doi = {10.48550/ARXIV.2212.05199},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
booktitle = {IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR)},
abstract = {We introduce the MAsked Generative VIdeo Transformer, MAGVIT, to tackle various video synthesis tasks with a single model. We introduce a 3D tokenizer to quantize a video into spatial-temporal visual tokens and propose an embedding method for masked video token modeling to facilitate multi-task learning. We conduct extensive experiments to demonstrate the quality, efficiency, and flexibility of MAGVIT. Our experiments show that (i) MAGVIT performs favorably against state-of-the-art approaches and establishes the best-published FVD on three video generation benchmarks, including the challenging Kinetics-600. (ii) MAGVIT outperforms existing methods in inference time by two orders of magnitude against diffusion models and by 60x against autoregressive models. (iii) A single MAGVIT model supports ten diverse generation tasks and generalizes across videos from different visual domains. The source code and trained models will be released to the public at this https URL.},
keywords = {computational video, computer vision, CVPR, generative AI, generative media, google},
pubstate = {published},
tppubtype = {inproceedings}
}
We introduce the MAsked Generative VIdeo Transformer, MAGVIT, to tackle various video synthesis tasks with a single model. We introduce a 3D tokenizer to quantize a video into spatial-temporal visual tokens and propose an embedding method for masked video token modeling to facilitate multi-task learning. We conduct extensive experiments to demonstrate the quality, efficiency, and flexibility of MAGVIT. Our experiments show that (i) MAGVIT performs favorably against state-of-the-art approaches and establishes the best-published FVD on three video generation benchmarks, including the challenging Kinetics-600. (ii) MAGVIT outperforms existing methods in inference time by two orders of magnitude against diffusion models and by 60x against autoregressive models. (iii) A single MAGVIT model supports ten diverse generation tasks and generalizes across videos from different visual domains. The source code and trained models will be released to the public at this https URL.
4.
Kihyuk Sohn, Yuan Hao, José Lezama, Luisa Polania, Huiwen Chang, Han Zhang, Irfan Essa, Lu Jiang
Visual Prompt Tuning for Generative Transfer Learning Proceedings Article
In: IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), 2023.
Abstract | Links | BibTeX | Tags: computer vision, CVPR, generative AI, generative media, google
@inproceedings{2022-Sohn-VPTGTL,
title = {Visual Prompt Tuning for Generative Transfer Learning},
author = {Kihyuk Sohn and Yuan Hao and José Lezama and Luisa Polania and Huiwen Chang and Han Zhang and Irfan Essa and Lu Jiang},
url = {https://arxiv.org/abs/2210.00990
https://openaccess.thecvf.com/content/CVPR2023/papers/Sohn_Visual_Prompt_Tuning_for_Generative_Transfer_Learning_CVPR_2023_paper.pdf
https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sohn_Visual_Prompt_Tuning_CVPR_2023_supplemental.pdf},
doi = {10.48550/ARXIV.2210.00990},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
booktitle = {IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR)},
abstract = {Transferring knowledge from an image synthesis model trained on a large dataset is a promising direction for learning generative image models from various domains efficiently. While previous works have studied GAN models, we present a recipe for learning vision transformers by generative knowledge transfer. We base our framework on state-of-the-art generative vision transformers that represent an image as a sequence of visual tokens to the autoregressive or non-autoregressive transformers. To adapt to a new domain, we employ prompt tuning, which prepends learnable tokens called prompt to the image token sequence, and introduce a new prompt design for our task. We study on a variety of visual domains, including visual task adaptation benchmark~citezhai2019large, with varying amount of training images, and show effectiveness of knowledge transfer and a significantly better image generation quality over existing works.},
keywords = {computer vision, CVPR, generative AI, generative media, google},
pubstate = {published},
tppubtype = {inproceedings}
}
Transferring knowledge from an image synthesis model trained on a large dataset is a promising direction for learning generative image models from various domains efficiently. While previous works have studied GAN models, we present a recipe for learning vision transformers by generative knowledge transfer. We base our framework on state-of-the-art generative vision transformers that represent an image as a sequence of visual tokens to the autoregressive or non-autoregressive transformers. To adapt to a new domain, we employ prompt tuning, which prepends learnable tokens called prompt to the image token sequence, and introduce a new prompt design for our task. We study on a variety of visual domains, including visual task adaptation benchmark~citezhai2019large, with varying amount of training images, and show effectiveness of knowledge transfer and a significantly better image generation quality over existing works.
5.
Kihyuk Sohn, Albert Shaw, Yuan Hao, Han Zhang, Luisa Polania, Huiwen Chang, Lu Jiang, Irfan Essa
Learning Disentangled Prompts for Compositional Image Synthesis Technical Report
2023.
Abstract | Links | BibTeX | Tags: arXiv, computer vision, generative AI, google, prompt engineering
@techreport{2023-Sohn-LDPCIS,
title = {Learning Disentangled Prompts for Compositional Image Synthesis},
author = {Kihyuk Sohn and Albert Shaw and Yuan Hao and Han Zhang and Luisa Polania and Huiwen Chang and Lu Jiang and Irfan Essa},
url = {https://arxiv.org/abs/2306.00763},
doi = { https://doi.org/10.48550/arXiv.2306.00763},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
abstract = {We study domain-adaptive image synthesis, the problem of teaching pretrained image generative models a new style or concept from as few as one image to synthesize novel images, to better understand the compositional image synthesis. We present a framework that leverages a pre-trained class-conditional generation model and visual prompt tuning. Specifically, we propose a novel source class distilled visual prompt that learns disentangled prompts of semantic (e.g., class) and domain (e.g., style) from a few images. Learned domain prompt is then used to synthesize images of any classes in the style of target domain. We conduct studies on various target domains with the number of images ranging from one to a few to many, and show qualitative results which show the compositional generalization of our method. Moreover, we show that our method can help improve zero-shot domain adaptation classification accuracy.
},
howpublished = {arXiv:2306.00763 },
keywords = {arXiv, computer vision, generative AI, google, prompt engineering},
pubstate = {published},
tppubtype = {techreport}
}
We study domain-adaptive image synthesis, the problem of teaching pretrained image generative models a new style or concept from as few as one image to synthesize novel images, to better understand the compositional image synthesis. We present a framework that leverages a pre-trained class-conditional generation model and visual prompt tuning. Specifically, we propose a novel source class distilled visual prompt that learns disentangled prompts of semantic (e.g., class) and domain (e.g., style) from a few images. Learned domain prompt is then used to synthesize images of any classes in the style of target domain. We conduct studies on various target domains with the number of images ranging from one to a few to many, and show qualitative results which show the compositional generalization of our method. Moreover, we show that our method can help improve zero-shot domain adaptation classification accuracy.
6.
Kihyuk Sohn, Nataniel Ruiz, Kimin Lee, Daniel Castro Chin, Irina Blok, Huiwen Chang, Jarred Barber, Lu Jiang, Glenn Entis, Yuanzhen Li, Yuan Hao, Irfan Essa, Michael Rubinstein, Dilip Krishnan
StyleDrop: Text-to-Image Generation in Any Style Technical Report
2023.
Abstract | Links | BibTeX | Tags: arXiv, computer vision, generative AI, google
@techreport{2023-Sohn-STGS,
title = {StyleDrop: Text-to-Image Generation in Any Style},
author = {Kihyuk Sohn and Nataniel Ruiz and Kimin Lee and Daniel Castro Chin and Irina Blok and Huiwen Chang and Jarred Barber and Lu Jiang and Glenn Entis and Yuanzhen Li and Yuan Hao and Irfan Essa and Michael Rubinstein and Dilip Krishnan},
url = {https://arxiv.org/abs/2306.00983},
doi = {10.48550/arXiv.2306.00983},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
abstract = {Pre-trained large text-to-image models synthesize impressive images with an appropriate use of text prompts. However, ambiguities inherent in natural language and out-of-distribution effects make it hard to synthesize image styles, that leverage a specific design pattern, texture or material. In this paper, we introduce StyleDrop, a method that enables the synthesis of images that faithfully follow a specific style using a text-to-image model. The proposed method is extremely versatile and captures nuances and details of a user-provided style, such as color schemes, shading, design patterns, and local and global effects. It efficiently learns a new style by fine-tuning very few trainable parameters (less than 1% of total model parameters) and improving the quality via iterative training with either human or automated feedback. Better yet, StyleDrop is able to deliver impressive results even when the user supplies only a single image that specifies the desired style. An extensive study shows that, for the task of style tuning text-to-image models, StyleDrop implemented on Muse convincingly outperforms other methods, including DreamBooth and textual inversion on Imagen or Stable Diffusion. More results are available at our project website: this https URL},
howpublished = {arXiv:2306.00983},
keywords = {arXiv, computer vision, generative AI, google},
pubstate = {published},
tppubtype = {techreport}
}
Pre-trained large text-to-image models synthesize impressive images with an appropriate use of text prompts. However, ambiguities inherent in natural language and out-of-distribution effects make it hard to synthesize image styles, that leverage a specific design pattern, texture or material. In this paper, we introduce StyleDrop, a method that enables the synthesis of images that faithfully follow a specific style using a text-to-image model. The proposed method is extremely versatile and captures nuances and details of a user-provided style, such as color schemes, shading, design patterns, and local and global effects. It efficiently learns a new style by fine-tuning very few trainable parameters (less than 1% of total model parameters) and improving the quality via iterative training with either human or automated feedback. Better yet, StyleDrop is able to deliver impressive results even when the user supplies only a single image that specifies the desired style. An extensive study shows that, for the task of style tuning text-to-image models, StyleDrop implemented on Muse convincingly outperforms other methods, including DreamBooth and textual inversion on Imagen or Stable Diffusion. More results are available at our project website: this https URL
7.
Harish Haresamudram, Irfan Essa, Thomas Ploetz
Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition Technical Report
2023.
@techreport{2023-Haresamudram-TLDRSWHAR,
title = {Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition},
author = {Harish Haresamudram and Irfan Essa and Thomas Ploetz},
year = {2023},
date = {2023-06-01},
abstract = {Human activity recognition (HAR) in wearable computing is typically based on direct processing of sensor data. Sensor readings are translated into representations, either derived through dedicated preprocessing, or integrated into end-to-end learning. Independent of their origin, for the vast majority of contemporary HAR, those representations are typically continuous in nature. That has not always been the case. In the early days of HAR, discretization approaches have been explored - primarily motivated by the desire to minimize computational requirements, but also with a view on applications beyond mere recognition, such as, activity discovery, fingerprinting, or large-scale search. Those traditional discretization approaches, however, suffer from substantial loss in precision and resolution in the resulting representations with detrimental effects on downstream tasks. Times have changed and in this paper we propose a return to discretized representations. We adopt and apply recent advancements in Vector Quantization (VQ) to wearables applications, which enables us to directly learn a mapping between short spans of sensor data and a codebook of vectors, resulting in recognition performance that is generally on par with their contemporary, continuous counterparts - sometimes surpassing them. Therefore, this work presents a proof-of-concept for demonstrating how effective discrete representations can be derived, enabling applications beyond mere activity classification but also opening up the field to advanced tools for the analysis of symbolic sequences, as they are known, for example, from domains such as natural language processing. Based on an extensive experimental evaluation on a suite of wearables-based benchmark HAR tasks, we demonstrate the potential of our learned discretization scheme and discuss how discretized sensor data analysis can lead to substantial changes in HAR.},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Human activity recognition (HAR) in wearable computing is typically based on direct processing of sensor data. Sensor readings are translated into representations, either derived through dedicated preprocessing, or integrated into end-to-end learning. Independent of their origin, for the vast majority of contemporary HAR, those representations are typically continuous in nature. That has not always been the case. In the early days of HAR, discretization approaches have been explored - primarily motivated by the desire to minimize computational requirements, but also with a view on applications beyond mere recognition, such as, activity discovery, fingerprinting, or large-scale search. Those traditional discretization approaches, however, suffer from substantial loss in precision and resolution in the resulting representations with detrimental effects on downstream tasks. Times have changed and in this paper we propose a return to discretized representations. We adopt and apply recent advancements in Vector Quantization (VQ) to wearables applications, which enables us to directly learn a mapping between short spans of sensor data and a codebook of vectors, resulting in recognition performance that is generally on par with their contemporary, continuous counterparts - sometimes surpassing them. Therefore, this work presents a proof-of-concept for demonstrating how effective discrete representations can be derived, enabling applications beyond mere activity classification but also opening up the field to advanced tools for the analysis of symbolic sequences, as they are known, for example, from domains such as natural language processing. Based on an extensive experimental evaluation on a suite of wearables-based benchmark HAR tasks, we demonstrate the potential of our learned discretization scheme and discuss how discretized sensor data analysis can lead to substantial changes in HAR.
8.
Lijun Yu, Yong Cheng, Zhiruo Wang, Vivek Kumar, Wolfgang Macherey, Yanping Huang, David A. Ross, Irfan Essa, Yonatan Bisk, Ming-Hsuan Yang, Kevin Murphy, Alexander G. Hauptmann, Lu Jiang
SPAE: Semantic Pyramid AutoEncoder for Multimodal Generation with Frozen LLMs Technical Report
2023.
Abstract | Links | BibTeX | Tags: arXiv, computational video, computer vision, generative AI
@techreport{2023-Yu-SSPAMGWFL,
title = {SPAE: Semantic Pyramid AutoEncoder for Multimodal Generation with Frozen LLMs},
author = {Lijun Yu and Yong Cheng and Zhiruo Wang and Vivek Kumar and Wolfgang Macherey and Yanping Huang and David A. Ross and Irfan Essa and Yonatan Bisk and Ming-Hsuan Yang and Kevin Murphy and Alexander G. Hauptmann and Lu Jiang},
url = {https://arxiv.org/abs/2306.17842},
doi = {10.48550/arXiv.2306.17842},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
abstract = {In this work, we introduce Semantic Pyramid AutoEncoder (SPAE) for enabling frozen LLMs to perform both understanding and generation tasks involving non-linguistic modalities such as images or videos. SPAE converts between raw pixels and interpretable lexical tokens (or words) extracted from the LLM's vocabulary. The resulting tokens capture both the semantic meaning and the fine-grained details needed for visual reconstruction, effectively translating the visual content into a language comprehensible to the LLM, and empowering it to perform a wide array of multimodal tasks. Our approach is validated through in-context learning experiments with frozen PaLM 2 and GPT 3.5 on a diverse set of image understanding and generation tasks. Our method marks the first successful attempt to enable a frozen LLM to generate image content while surpassing state-of-the-art performance in image understanding tasks, under the same setting, by over 25%.},
howpublished = {arXiv:2306.17842v2},
keywords = {arXiv, computational video, computer vision, generative AI},
pubstate = {published},
tppubtype = {techreport}
}
In this work, we introduce Semantic Pyramid AutoEncoder (SPAE) for enabling frozen LLMs to perform both understanding and generation tasks involving non-linguistic modalities such as images or videos. SPAE converts between raw pixels and interpretable lexical tokens (or words) extracted from the LLM's vocabulary. The resulting tokens capture both the semantic meaning and the fine-grained details needed for visual reconstruction, effectively translating the visual content into a language comprehensible to the LLM, and empowering it to perform a wide array of multimodal tasks. Our approach is validated through in-context learning experiments with frozen PaLM 2 and GPT 3.5 on a diverse set of image understanding and generation tasks. Our method marks the first successful attempt to enable a frozen LLM to generate image content while surpassing state-of-the-art performance in image understanding tasks, under the same setting, by over 25%.
9.
José Lezama, Tim Salimans, Lu Jiang, Huiwen Chang, Jonathan Ho, Irfan Essa
Discrete Predictor-Corrector Diffusion Models for Image Synthesis Proceedings Article
In: International Conference on Learning Representations (ICLR), 2023.
Abstract | Links | BibTeX | Tags: computer vision, generative AI, generative media, google, ICLR, machine learning
@inproceedings{2023-Lezama-DPDMIS,
title = {Discrete Predictor-Corrector Diffusion Models for Image Synthesis},
author = {José Lezama and Tim Salimans and Lu Jiang and Huiwen Chang and Jonathan Ho and Irfan Essa},
url = {https://openreview.net/forum?id=VM8batVBWvg},
year = {2023},
date = {2023-05-01},
urldate = {2023-05-01},
booktitle = {International Conference on Learning Representations (ICLR)},
abstract = {We introduce Discrete Predictor-Corrector diffusion models (DPC), extending predictor-corrector samplers in Gaussian diffusion models to the discrete case. Predictor-corrector samplers are a class of samplers for diffusion models, which improve on ancestral samplers by correcting the sampling distribution of intermediate diffusion states using MCMC methods. In DPC, the Langevin corrector, which does not have a direct counterpart in discrete space, is replaced with a discrete MCMC transition defined by a learned corrector kernel. The corrector kernel is trained to make the correction steps achieve asymptotic convergence, in distribution, to the correct marginal of the intermediate diffusion states. Equipped with DPC, we revisit recent transformer-based non-autoregressive generative models through the lens of discrete diffusion, and find that DPC can alleviate the compounding decoding error due to the parallel sampling of visual tokens. Our experiments show that DPC improves upon existing discrete latent space models for class-conditional image generation on ImageNet, and outperforms continuous diffusion models and GANs, according to standard metrics and user preference studies},
keywords = {computer vision, generative AI, generative media, google, ICLR, machine learning},
pubstate = {published},
tppubtype = {inproceedings}
}
We introduce Discrete Predictor-Corrector diffusion models (DPC), extending predictor-corrector samplers in Gaussian diffusion models to the discrete case. Predictor-corrector samplers are a class of samplers for diffusion models, which improve on ancestral samplers by correcting the sampling distribution of intermediate diffusion states using MCMC methods. In DPC, the Langevin corrector, which does not have a direct counterpart in discrete space, is replaced with a discrete MCMC transition defined by a learned corrector kernel. The corrector kernel is trained to make the correction steps achieve asymptotic convergence, in distribution, to the correct marginal of the intermediate diffusion states. Equipped with DPC, we revisit recent transformer-based non-autoregressive generative models through the lens of discrete diffusion, and find that DPC can alleviate the compounding decoding error due to the parallel sampling of visual tokens. Our experiments show that DPC improves upon existing discrete latent space models for class-conditional image generation on ImageNet, and outperforms continuous diffusion models and GANs, according to standard metrics and user preference studies
10.
Erik Wijmans, Manolis Savva, Irfan Essa, Stefan Lee, Ari S. Morcos, Dhruv Batra
Emergence of Maps in the Memories of Blind Navigation Agents Best Paper Proceedings Article
In: Proceedings of International Conference on Learning Representations (ICLR), 2023.
Abstract | Links | BibTeX | Tags: awards, best paper award, computer vision, google, ICLR, machine learning, robotics
@inproceedings{2023-Wijmans-EMMBNA,
title = {Emergence of Maps in the Memories of Blind Navigation Agents},
author = {Erik Wijmans and Manolis Savva and Irfan Essa and Stefan Lee and Ari S. Morcos and Dhruv Batra},
url = {https://arxiv.org/abs/2301.13261
https://wijmans.xyz/publication/eom/
https://openreview.net/forum?id=lTt4KjHSsyl
https://blog.iclr.cc/2023/03/21/announcing-the-iclr-2023-outstanding-paper-award-recipients/},
doi = {10.48550/ARXIV.2301.13261},
year = {2023},
date = {2023-05-01},
urldate = {2023-05-01},
booktitle = {Proceedings of International Conference on Learning Representations (ICLR)},
abstract = {Animal navigation research posits that organisms build and maintain internal spatial representations, or maps, of their environment. We ask if machines -- specifically, artificial intelligence (AI) navigation agents -- also build implicit (or 'mental') maps. A positive answer to this question would (a) explain the surprising phenomenon in recent literature of ostensibly map-free neural-networks achieving strong performance, and (b) strengthen the evidence of mapping as a fundamental mechanism for navigation by intelligent embodied agents, whether they be biological or artificial. Unlike animal navigation, we can judiciously design the agent's perceptual system and control the learning paradigm to nullify alternative navigation mechanisms. Specifically, we train 'blind' agents -- with sensing limited to only egomotion and no other sensing of any kind -- to perform PointGoal navigation ('go to Δ x, Δ y') via reinforcement learning. Our agents are composed of navigation-agnostic components (fully-connected and recurrent neural networks), and our experimental setup provides no inductive bias towards mapping. Despite these harsh conditions, we find that blind agents are (1) surprisingly effective navigators in new environments (~95% success); (2) they utilize memory over long horizons (remembering ~1,000 steps of past experience in an episode); (3) this memory enables them to exhibit intelligent behavior (following walls, detecting collisions, taking shortcuts); (4) there is emergence of maps and collision detection neurons in the representations of the environment built by a blind agent as it navigates; and (5) the emergent maps are selective and task dependent (e.g. the agent 'forgets' exploratory detours). Overall, this paper presents no new techniques for the AI audience, but a surprising finding, an insight, and an explanation.},
keywords = {awards, best paper award, computer vision, google, ICLR, machine learning, robotics},
pubstate = {published},
tppubtype = {inproceedings}
}
Animal navigation research posits that organisms build and maintain internal spatial representations, or maps, of their environment. We ask if machines -- specifically, artificial intelligence (AI) navigation agents -- also build implicit (or 'mental') maps. A positive answer to this question would (a) explain the surprising phenomenon in recent literature of ostensibly map-free neural-networks achieving strong performance, and (b) strengthen the evidence of mapping as a fundamental mechanism for navigation by intelligent embodied agents, whether they be biological or artificial. Unlike animal navigation, we can judiciously design the agent's perceptual system and control the learning paradigm to nullify alternative navigation mechanisms. Specifically, we train 'blind' agents -- with sensing limited to only egomotion and no other sensing of any kind -- to perform PointGoal navigation ('go to Δ x, Δ y') via reinforcement learning. Our agents are composed of navigation-agnostic components (fully-connected and recurrent neural networks), and our experimental setup provides no inductive bias towards mapping. Despite these harsh conditions, we find that blind agents are (1) surprisingly effective navigators in new environments (~95% success); (2) they utilize memory over long horizons (remembering ~1,000 steps of past experience in an episode); (3) this memory enables them to exhibit intelligent behavior (following walls, detecting collisions, taking shortcuts); (4) there is emergence of maps and collision detection neurons in the representations of the environment built by a blind agent as it navigates; and (5) the emergent maps are selective and task dependent (e.g. the agent 'forgets' exploratory detours). Overall, this paper presents no new techniques for the AI audience, but a surprising finding, an insight, and an explanation.
11.
Yi-Hao Peng, Peggy Chi, Anjuli Kannan, Meredith Morris, Irfan Essa
Slide Gestalt: Automatic Structure Extraction in Slide Decks for Non-Visual Access Proceedings Article
In: ACM Symposium on User Interface Software and Technology (UIST), 2023.
Abstract | Links | BibTeX | Tags: accessibility, CHI, google, human-computer interaction
@inproceedings{2023-Peng-SGASESDNA,
title = {Slide Gestalt: Automatic Structure Extraction in Slide Decks for Non-Visual Access},
author = {Yi-Hao Peng and Peggy Chi and Anjuli Kannan and Meredith Morris and Irfan Essa},
url = {https://research.google/pubs/pub52182/
https://dl.acm.org/doi/fullHtml/10.1145/3544548.3580921
https://doi.org/10.1145/3544548.3580921
https://www.youtube.com/watch?v=pK08aMRx4qo},
year = {2023},
date = {2023-04-23},
urldate = {2023-04-23},
booktitle = {ACM Symposium on User Interface Software and Technology (UIST)},
abstract = {Presentation slides commonly use visual patterns for structural navigation, such as titles, dividers, and build slides. However, screen readers do not capture such intention, making it time-consuming and less accessible for blind and visually impaired (BVI) users to linearly consume slides with repeated content. We present Slide Gestalt, an automatic approach that identifies the hierarchical structure in a slide deck. Slide Gestalt computes the visual and textual correspondences between slides to generate hierarchical groupings. Readers can navigate the slide deck from the higher-level section overview to the lower-level description of a slide group or individual elements interactively with our UI. We derived side consumption and authoring practices from interviews with BVI readers and sighted creators and an analysis of 100 decks. We performed our pipeline with 50 real-world slide decks and a large dataset. Feedback from eight BVI participants showed that Slide Gestalt helped navigate a slide deck by anchoring content more efficiently, compared to using accessible slides.},
keywords = {accessibility, CHI, google, human-computer interaction},
pubstate = {published},
tppubtype = {inproceedings}
}
Presentation slides commonly use visual patterns for structural navigation, such as titles, dividers, and build slides. However, screen readers do not capture such intention, making it time-consuming and less accessible for blind and visually impaired (BVI) users to linearly consume slides with repeated content. We present Slide Gestalt, an automatic approach that identifies the hierarchical structure in a slide deck. Slide Gestalt computes the visual and textual correspondences between slides to generate hierarchical groupings. Readers can navigate the slide deck from the higher-level section overview to the lower-level description of a slide group or individual elements interactively with our UI. We derived side consumption and authoring practices from interviews with BVI readers and sighted creators and an analysis of 100 decks. We performed our pipeline with 50 real-world slide decks and a large dataset. Feedback from eight BVI participants showed that Slide Gestalt helped navigate a slide deck by anchoring content more efficiently, compared to using accessible slides.
12.
Karan Samel, Jun Ma, Zhengyang Wang, Tong Zhao, Irfan Essa
Knowledge Relevance BERT: Integrating Noisy Knowledge into Language Representation. Proceedings Article
In: AAAI workshop on Knowledge Augmented Methods for NLP (KnowledgeNLP-AAAI 2023), 2023.
Abstract | Links | BibTeX | Tags: AI, knowledge representation, NLP
@inproceedings{2023-Samel-KRBINKILR,
title = {Knowledge Relevance BERT: Integrating Noisy Knowledge into Language Representation.},
author = {Karan Samel and Jun Ma and Zhengyang Wang and Tong Zhao and Irfan Essa},
url = {https://knowledge-nlp.github.io/aaai2023/papers/005-KRBERT-oral.pdf},
year = {2023},
date = {2023-02-01},
urldate = {2023-02-01},
booktitle = {AAAI workshop on Knowledge Augmented Methods for NLP (KnowledgeNLP-AAAI 2023)},
abstract = {Integrating structured knowledge into language model representations increases recall of domain-specific information useful for downstream tasks. Matching between knowledge graph entities and text entity mentions can be easily performed when entity names are unique or entity-linking data exists. When extending this setting to new domains, newly mined knowledge contains ambiguous and incorrect information without explicit linking information. In such settings, we design a framework to robustly link relevant knowledge to input texts as an intermediate modeling step while performing end-to-end domain fine-tuning tasks. This is done by first computing the similarity of the existing task labels with candidate knowledge triplets to generate relevance labels. We use these labels to train a relevance model, which predicts the relevance of the inserted triplets to the original text. This relevance model is integrated within a language model, leading to our Knowledge Relevance BERT (KR-BERT) framework. We test KR-BERT for linking and ranking tasks on a real-world e-commerce dataset and a public entity linking task, where we show performance improvements over strong baselines.},
keywords = {AI, knowledge representation, NLP},
pubstate = {published},
tppubtype = {inproceedings}
}
Integrating structured knowledge into language model representations increases recall of domain-specific information useful for downstream tasks. Matching between knowledge graph entities and text entity mentions can be easily performed when entity names are unique or entity-linking data exists. When extending this setting to new domains, newly mined knowledge contains ambiguous and incorrect information without explicit linking information. In such settings, we design a framework to robustly link relevant knowledge to input texts as an intermediate modeling step while performing end-to-end domain fine-tuning tasks. This is done by first computing the similarity of the existing task labels with candidate knowledge triplets to generate relevance labels. We use these labels to train a relevance model, which predicts the relevance of the inserted triplets to the original text. This relevance model is integrated within a language model, leading to our Knowledge Relevance BERT (KR-BERT) framework. We test KR-BERT for linking and ranking tasks on a real-world e-commerce dataset and a public entity linking task, where we show performance improvements over strong baselines.
13.
Tianhao Zhang, Weilong Yang, Honglak Lee, Hung-Yu Tseng, Irfan Essa, Lu Jiang
Image manipulation by text instruction Patent
2023.
Abstract | Links | BibTeX | Tags: content creation, generative AI, google, media generation, patents
@patent{2023-Zhang-IMTI,
title = {Image manipulation by text instruction},
author = {Tianhao Zhang and Weilong Yang and Honglak Lee and Hung-Yu Tseng and Irfan Essa and Lu Jiang},
url = {https://patents.google.com/patent/US11562518},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
abstract = {A method for generating an output image from an input image and an input text instruction that specifies a location and a modification of an edit applied to the input image using a neural network is described. The neural network includes an image encoder, an image decoder, and an instruction attention network. The method includes receiving the input image and the input text instruction; extracting, from the input image, an input image feature that represents features of the input image using the image encoder; generating a spatial feature and a modification feature from the input text instruction using the instruction attention network; generating an edited image feature from the input image feature, the spatial feature and the modification feature; and generating the output image from the edited image feature using the image decoder.},
howpublished = {US Patent # US11562518},
keywords = {content creation, generative AI, google, media generation, patents},
pubstate = {published},
tppubtype = {patent}
}
A method for generating an output image from an input image and an input text instruction that specifies a location and a modification of an edit applied to the input image using a neural network is described. The neural network includes an image encoder, an image decoder, and an instruction attention network. The method includes receiving the input image and the input text instruction; extracting, from the input image, an input image feature that represents features of the input image using the image encoder; generating a spatial feature and a modification feature from the input text instruction using the instruction attention network; generating an edited image feature from the input image feature, the spatial feature and the modification feature; and generating the output image from the edited image feature using the image decoder.
14.
Erik Wijmans, Irfan Essa, Dhruv Batra
How to Train PointGoal Navigation Agents on a (Sample and Compute) Budget Proceedings Article
In: International Conference on Autonomous Agents and Multi-Agent Systems, 2022.
Abstract | Links | BibTeX | Tags: computer vision, embodied agents, navigation
@inproceedings{2022-Wijmans-TPNASCB,
title = {How to Train PointGoal Navigation Agents on a (Sample and Compute) Budget},
author = {Erik Wijmans and Irfan Essa and Dhruv Batra},
url = {https://arxiv.org/abs/2012.06117
https://ifaamas.org/Proceedings/aamas2022/pdfs/p1762.pdf},
doi = {10.48550/arXiv.2012.06117},
year = {2022},
date = {2022-12-01},
urldate = {2020-12-01},
booktitle = {International Conference on Autonomous Agents and Multi-Agent Systems},
journal = {arXiv},
number = {arXiv:2012.06117},
abstract = {PointGoal navigation has seen significant recent interest and progress, spurred on by the Habitat platform and associated challenge. In this paper, we study PointGoal navigation under both a sample budget (75 million frames) and a compute budget (1 GPU for 1 day). We conduct an extensive set of experiments, cumulatively totaling over 50,000 GPU-hours, that let us identify and discuss a number of ostensibly minor but significant design choices -- the advantage estimation procedure (a key component in training), visual encoder architecture, and a seemingly minor hyper-parameter change. Overall, these design choices to lead considerable and consistent improvements over the baselines present in Savva et al. Under a sample budget, performance for RGB-D agents improves 8 SPL on Gibson (14% relative improvement) and 20 SPL on Matterport3D (38% relative improvement). Under a compute budget, performance for RGB-D agents improves by 19 SPL on Gibson (32% relative improvement) and 35 SPL on Matterport3D (220% relative improvement). We hope our findings and recommendations will make serve to make the community's experiments more efficient.},
keywords = {computer vision, embodied agents, navigation},
pubstate = {published},
tppubtype = {inproceedings}
}
PointGoal navigation has seen significant recent interest and progress, spurred on by the Habitat platform and associated challenge. In this paper, we study PointGoal navigation under both a sample budget (75 million frames) and a compute budget (1 GPU for 1 day). We conduct an extensive set of experiments, cumulatively totaling over 50,000 GPU-hours, that let us identify and discuss a number of ostensibly minor but significant design choices -- the advantage estimation procedure (a key component in training), visual encoder architecture, and a seemingly minor hyper-parameter change. Overall, these design choices to lead considerable and consistent improvements over the baselines present in Savva et al. Under a sample budget, performance for RGB-D agents improves 8 SPL on Gibson (14% relative improvement) and 20 SPL on Matterport3D (38% relative improvement). Under a compute budget, performance for RGB-D agents improves by 19 SPL on Gibson (32% relative improvement) and 35 SPL on Matterport3D (220% relative improvement). We hope our findings and recommendations will make serve to make the community's experiments more efficient.
15.
Erik Wijmans, Irfan Essa, Dhruv Batra
VER: Scaling On-Policy RL Leads to the Emergence of Navigation in Embodied Rearrangement Proceedings Article
In: Oh, Alice H., Agarwal, Alekh, Belgrave, Danielle, Cho, Kyunghyun (Ed.): Advances in Neural Information Processing Systems (NeurIPS), 2022.
Abstract | Links | BibTeX | Tags: machine learning, NeurIPS, reinforcement learning, robotics
@inproceedings{2022-Wijmans-SOLENER,
title = {VER: Scaling On-Policy RL Leads to the Emergence of Navigation in Embodied Rearrangement},
author = {Erik Wijmans and Irfan Essa and Dhruv Batra},
editor = {Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho},
url = {https://arxiv.org/abs/2210.05064
https://openreview.net/forum?id=VrJWseIN98},
doi = {10.48550/ARXIV.2210.05064},
year = {2022},
date = {2022-12-01},
urldate = {2022-12-01},
booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
abstract = {We present Variable Experience Rollout (VER), a technique for efficiently scaling batched on-policy reinforcement learning in heterogenous environments (where different environments take vastly different times to generate rollouts) to many GPUs residing on, potentially, many machines. VER combines the strengths of and blurs the line between synchronous and asynchronous on-policy RL methods (SyncOnRL and AsyncOnRL, respectively). Specifically, it learns from on-policy experience (like SyncOnRL) and has no synchronization points (like AsyncOnRL) enabling high throughput.
We find that VER leads to significant and consistent speed-ups across a broad range of embodied navigation and mobile manipulation tasks in photorealistic 3D simulation environments. Specifically, for PointGoal navigation and ObjectGoal navigation in Habitat 1.0, VER is 60-100% faster (1.6-2x speedup) than DD-PPO, the current state of art for distributed SyncOnRL, with similar sample efficiency. For mobile manipulation tasks (open fridge/cabinet, pick/place objects) in Habitat 2.0 VER is 150% faster (2.5x speedup) on 1 GPU and 170% faster (2.7x speedup) on 8 GPUs than DD-PPO. Compared to SampleFactory (the current state-of-the-art AsyncOnRL), VER matches its speed on 1 GPU, and is 70% faster (1.7x speedup) on 8 GPUs with better sample efficiency.
We leverage these speed-ups to train chained skills for GeometricGoal rearrangement tasks in the Home Assistant Benchmark (HAB). We find a surprising emergence of navigation in skills that do not ostensible require any navigation. Specifically, the Pick skill involves a robot picking an object from a table. During training the robot was always spawned close to the table and never needed to navigate. However, we find that if base movement is part of the action space, the robot learns to navigate then pick an object in new environments with 50% success, demonstrating surprisingly high out-of-distribution generalization.},
keywords = {machine learning, NeurIPS, reinforcement learning, robotics},
pubstate = {published},
tppubtype = {inproceedings}
}
We present Variable Experience Rollout (VER), a technique for efficiently scaling batched on-policy reinforcement learning in heterogenous environments (where different environments take vastly different times to generate rollouts) to many GPUs residing on, potentially, many machines. VER combines the strengths of and blurs the line between synchronous and asynchronous on-policy RL methods (SyncOnRL and AsyncOnRL, respectively). Specifically, it learns from on-policy experience (like SyncOnRL) and has no synchronization points (like AsyncOnRL) enabling high throughput.
We find that VER leads to significant and consistent speed-ups across a broad range of embodied navigation and mobile manipulation tasks in photorealistic 3D simulation environments. Specifically, for PointGoal navigation and ObjectGoal navigation in Habitat 1.0, VER is 60-100% faster (1.6-2x speedup) than DD-PPO, the current state of art for distributed SyncOnRL, with similar sample efficiency. For mobile manipulation tasks (open fridge/cabinet, pick/place objects) in Habitat 2.0 VER is 150% faster (2.5x speedup) on 1 GPU and 170% faster (2.7x speedup) on 8 GPUs than DD-PPO. Compared to SampleFactory (the current state-of-the-art AsyncOnRL), VER matches its speed on 1 GPU, and is 70% faster (1.7x speedup) on 8 GPUs with better sample efficiency.
We leverage these speed-ups to train chained skills for GeometricGoal rearrangement tasks in the Home Assistant Benchmark (HAB). We find a surprising emergence of navigation in skills that do not ostensible require any navigation. Specifically, the Pick skill involves a robot picking an object from a table. During training the robot was always spawned close to the table and never needed to navigate. However, we find that if base movement is part of the action space, the robot learns to navigate then pick an object in new environments with 50% success, demonstrating surprisingly high out-of-distribution generalization.
We find that VER leads to significant and consistent speed-ups across a broad range of embodied navigation and mobile manipulation tasks in photorealistic 3D simulation environments. Specifically, for PointGoal navigation and ObjectGoal navigation in Habitat 1.0, VER is 60-100% faster (1.6-2x speedup) than DD-PPO, the current state of art for distributed SyncOnRL, with similar sample efficiency. For mobile manipulation tasks (open fridge/cabinet, pick/place objects) in Habitat 2.0 VER is 150% faster (2.5x speedup) on 1 GPU and 170% faster (2.7x speedup) on 8 GPUs than DD-PPO. Compared to SampleFactory (the current state-of-the-art AsyncOnRL), VER matches its speed on 1 GPU, and is 70% faster (1.7x speedup) on 8 GPUs with better sample efficiency.
We leverage these speed-ups to train chained skills for GeometricGoal rearrangement tasks in the Home Assistant Benchmark (HAB). We find a surprising emergence of navigation in skills that do not ostensible require any navigation. Specifically, the Pick skill involves a robot picking an object from a table. During training the robot was always spawned close to the table and never needed to navigate. However, we find that if base movement is part of the action space, the robot learns to navigate then pick an object in new environments with 50% success, demonstrating surprisingly high out-of-distribution generalization.
16.
Huda Alamri, Anthony Bilic, Michael Hu, Apoorva Beedu, Irfan Essa
End-to-end Multimodal Representation Learning for Video Dialog Proceedings Article
In: NeuRIPS Workshop on Vision Transformers: Theory and applications, 2022.
Abstract | Links | BibTeX | Tags: computational video, computer vision, vision transformers
@inproceedings{2022-Alamri-EMRLVD,
title = {End-to-end Multimodal Representation Learning for Video Dialog},
author = {Huda Alamri and Anthony Bilic and Michael Hu and Apoorva Beedu and Irfan Essa},
url = {https://arxiv.org/abs/2210.14512},
doi = {10.48550/arXiv.2210.14512},
year = {2022},
date = {2022-12-01},
urldate = {2022-12-01},
booktitle = {NeuRIPS Workshop on Vision Transformers: Theory and applications},
abstract = {Video-based dialog task is a challenging multimodal learning task that has received increasing attention over the past few years with state-of-the-art obtaining new performance records. This progress is largely powered by the adaptation of the more powerful transformer-based language encoders. Despite this progress, existing approaches do not effectively utilize visual features to help solve tasks. Recent studies show that state-of-the-art models are biased towards textual information rather than visual cues. In order to better leverage the available visual information, this study proposes a new framework that combines 3D-CNN network and transformer-based networks into a single visual encoder to extract more robust semantic representations from videos. The visual encoder is jointly trained end-to-end with other input modalities such as text and audio. Experiments on the AVSD task show significant improvement over baselines in both generative and retrieval tasks.},
keywords = {computational video, computer vision, vision transformers},
pubstate = {published},
tppubtype = {inproceedings}
}
Video-based dialog task is a challenging multimodal learning task that has received increasing attention over the past few years with state-of-the-art obtaining new performance records. This progress is largely powered by the adaptation of the more powerful transformer-based language encoders. Despite this progress, existing approaches do not effectively utilize visual features to help solve tasks. Recent studies show that state-of-the-art models are biased towards textual information rather than visual cues. In order to better leverage the available visual information, this study proposes a new framework that combines 3D-CNN network and transformer-based networks into a single visual encoder to extract more robust semantic representations from videos. The visual encoder is jointly trained end-to-end with other input modalities such as text and audio. Experiments on the AVSD task show significant improvement over baselines in both generative and retrieval tasks.
17.
Apoorva Beedu, Huda Alamri, Irfan Essa
Video based Object 6D Pose Estimation using Transformers Proceedings Article
In: NeuRIPS Workshop on Vision Transformers: Theory and applications, 2022.
Abstract | Links | BibTeX | Tags: computer vision, vision transformers
@inproceedings{2022-Beedu-VBOPEUT,
title = {Video based Object 6D Pose Estimation using Transformers},
author = {Apoorva Beedu and Huda Alamri and Irfan Essa},
url = {https://arxiv.org/abs/2210.13540},
doi = {10.48550/arXiv.2210.13540},
year = {2022},
date = {2022-12-01},
urldate = {2022-12-01},
booktitle = {NeuRIPS Workshop on Vision Transformers: Theory and applications},
abstract = {We introduce a Transformer based 6D Object Pose Estimation framework VideoPose, comprising an end-to-end attention based modelling architecture, that attends to previous frames in order to estimate accurate 6D Object Poses in videos. Our approach leverages the temporal information from a video sequence for pose refinement, along with being computationally efficient and robust. Compared to existing methods, our architecture is able to capture and reason from long-range dependencies efficiently, thus iteratively refining over video sequences.Experimental evaluation on the YCB-Video dataset shows that our approach is on par with the state-of-the-art Transformer methods, and performs significantly better relative to CNN based approaches. Further, with a speed of 33 fps, it is also more efficient and therefore applicable to a variety of applications that require real-time object pose estimation. Training code and pretrained models are available at https://anonymous.4open.science/r/VideoPose-3C8C.},
keywords = {computer vision, vision transformers},
pubstate = {published},
tppubtype = {inproceedings}
}
We introduce a Transformer based 6D Object Pose Estimation framework VideoPose, comprising an end-to-end attention based modelling architecture, that attends to previous frames in order to estimate accurate 6D Object Poses in videos. Our approach leverages the temporal information from a video sequence for pose refinement, along with being computationally efficient and robust. Compared to existing methods, our architecture is able to capture and reason from long-range dependencies efficiently, thus iteratively refining over video sequences.Experimental evaluation on the YCB-Video dataset shows that our approach is on par with the state-of-the-art Transformer methods, and performs significantly better relative to CNN based approaches. Further, with a speed of 33 fps, it is also more efficient and therefore applicable to a variety of applications that require real-time object pose estimation. Training code and pretrained models are available at https://anonymous.4open.science/r/VideoPose-3C8C.
18.
José Lezama, Huiwen Chang, Lu Jiang, Irfan Essa
Improved Masked Image Generation with Token-Critic Proceedings Article
In: European Conference on Computer Vision (ECCV), arXiv, 2022, ISBN: 978-3-031-20050-2.
Abstract | Links | BibTeX | Tags: computer vision, ECCV, generative AI, generative media, google
@inproceedings{2022-Lezama-IMIGWT,
title = {Improved Masked Image Generation with Token-Critic},
author = {José Lezama and Huiwen Chang and Lu Jiang and Irfan Essa},
url = {https://arxiv.org/abs/2209.04439
https://rdcu.be/c61MZ},
doi = {10.1007/978-3-031-20050-2_5},
isbn = {978-3-031-20050-2},
year = {2022},
date = {2022-10-28},
urldate = {2022-10-28},
booktitle = {European Conference on Computer Vision (ECCV)},
volume = {13683},
publisher = {arXiv},
abstract = {Non-autoregressive generative transformers recently demonstrated impressive image generation performance, and orders of magnitude faster sampling than their autoregressive counterparts. However, optimal parallel sampling from the true joint distribution of visual tokens remains an open challenge. In this paper we introduce Token-Critic, an auxiliary model to guide the sampling of a non-autoregressive generative transformer. Given a masked-and-reconstructed real image, the Token-Critic model is trained to distinguish which visual tokens belong to the original image and which were sampled by the generative transformer. During non-autoregressive iterative sampling, Token-Critic is used to select which tokens to accept and which to reject and resample. Coupled with Token-Critic, a state-of-the-art generative transformer significantly improves its performance, and outperforms recent diffusion models and GANs in terms of the trade-off between generated image quality and diversity, in the challenging class-conditional ImageNet generation.},
keywords = {computer vision, ECCV, generative AI, generative media, google},
pubstate = {published},
tppubtype = {inproceedings}
}
Non-autoregressive generative transformers recently demonstrated impressive image generation performance, and orders of magnitude faster sampling than their autoregressive counterparts. However, optimal parallel sampling from the true joint distribution of visual tokens remains an open challenge. In this paper we introduce Token-Critic, an auxiliary model to guide the sampling of a non-autoregressive generative transformer. Given a masked-and-reconstructed real image, the Token-Critic model is trained to distinguish which visual tokens belong to the original image and which were sampled by the generative transformer. During non-autoregressive iterative sampling, Token-Critic is used to select which tokens to accept and which to reject and resample. Coupled with Token-Critic, a state-of-the-art generative transformer significantly improves its performance, and outperforms recent diffusion models and GANs in terms of the trade-off between generated image quality and diversity, in the challenging class-conditional ImageNet generation.
19.
Xiang Kong, Lu Jiang, Huiwen Chang, Han Zhang, Yuan Hao, Haifeng Gong, Irfan Essa
BLT: Bidirectional Layout Transformer for Controllable Layout Generation Proceedings Article
In: European Conference on Computer Vision (ECCV), 2022, ISBN: 978-3-031-19789-5.
Abstract | Links | BibTeX | Tags: computer vision, ECCV, generative AI, generative media, google, vision transformer
@inproceedings{2022-Kong-BLTCLG,
title = {BLT: Bidirectional Layout Transformer for Controllable Layout Generation},
author = {Xiang Kong and Lu Jiang and Huiwen Chang and Han Zhang and Yuan Hao and Haifeng Gong and Irfan Essa},
url = {https://arxiv.org/abs/2112.05112
https://rdcu.be/c61AE},
doi = {10.1007/978-3-031-19790-1_29},
isbn = {978-3-031-19789-5},
year = {2022},
date = {2022-10-25},
urldate = {2022-10-25},
booktitle = {European Conference on Computer Vision (ECCV)},
volume = {13677},
abstract = {Creating visual layouts is a critical step in graphic design. Automatic generation of such layouts is essential for scalable and diverse visual designs. To advance conditional layout generation, we introduce BLT, a bidirectional layout transformer. BLT differs from previous work on transformers in adopting non-autoregressive transformers. In training, BLT learns to predict the masked attributes by attending to surrounding attributes in two directions. During inference, BLT first generates a draft layout from the input and then iteratively refines it into a high-quality layout by masking out low-confident attributes. The masks generated in both training and inference are controlled by a new hierarchical sampling policy. We verify the proposed model on six benchmarks of diverse design tasks. Experimental results demonstrate two benefits compared to the state-of-the-art layout transformer models. First, our model empowers layout transformers to fulfill controllable layout generation. Second, it achieves up to 10x speedup in generating a layout at inference time than the layout transformer baseline. Code is released at https://shawnkx.github.io/blt.},
keywords = {computer vision, ECCV, generative AI, generative media, google, vision transformer},
pubstate = {published},
tppubtype = {inproceedings}
}
Creating visual layouts is a critical step in graphic design. Automatic generation of such layouts is essential for scalable and diverse visual designs. To advance conditional layout generation, we introduce BLT, a bidirectional layout transformer. BLT differs from previous work on transformers in adopting non-autoregressive transformers. In training, BLT learns to predict the masked attributes by attending to surrounding attributes in two directions. During inference, BLT first generates a draft layout from the input and then iteratively refines it into a high-quality layout by masking out low-confident attributes. The masks generated in both training and inference are controlled by a new hierarchical sampling policy. We verify the proposed model on six benchmarks of diverse design tasks. Experimental results demonstrate two benefits compared to the state-of-the-art layout transformer models. First, our model empowers layout transformers to fulfill controllable layout generation. Second, it achieves up to 10x speedup in generating a layout at inference time than the layout transformer baseline. Code is released at https://shawnkx.github.io/blt.
20.
Peggy Chi, Tao Dong, Christian Frueh, Brian Colonna, Vivek Kwatra, Irfan Essa
Synthesis-Assisted Video Prototyping From a Document Proceedings Article
In: Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology, pp. 1–10, 2022.
Abstract | Links | BibTeX | Tags: computational video, generative media, google, human-computer interaction, UIST, video editing
@inproceedings{2022-Chi-SVPFD,
title = {Synthesis-Assisted Video Prototyping From a Document},
author = {Peggy Chi and Tao Dong and Christian Frueh and Brian Colonna and Vivek Kwatra and Irfan Essa},
url = {https://research.google/pubs/pub51631/
https://dl.acm.org/doi/abs/10.1145/3526113.3545676},
doi = {10.1145/3526113.3545676},
year = {2022},
date = {2022-10-01},
urldate = {2022-10-01},
booktitle = {Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology},
pages = {1--10},
abstract = {Video productions commonly start with a script, especially for talking head videos that feature a speaker narrating to the camera. When the source materials come from a written document -- such as a web tutorial, it takes iterations to refine content from a text article to a spoken dialogue, while considering visual compositions in each scene. We propose Doc2Video, a video prototyping approach that converts a document to interactive scripting with a preview of synthetic talking head videos. Our pipeline decomposes a source document into a series of scenes, each automatically creating a synthesized video of a virtual instructor. Designed for a specific domain -- programming cookbooks, we apply visual elements from the source document, such as a keyword, a code snippet or a screenshot, in suitable layouts. Users edit narration sentences, break or combine sections, and modify visuals to prototype a video in our Editing UI. We evaluated our pipeline with public programming cookbooks. Feedback from professional creators shows that our method provided a reasonable starting point to engage them in interactive scripting for a narrated instructional video.},
keywords = {computational video, generative media, google, human-computer interaction, UIST, video editing},
pubstate = {published},
tppubtype = {inproceedings}
}
Video productions commonly start with a script, especially for talking head videos that feature a speaker narrating to the camera. When the source materials come from a written document -- such as a web tutorial, it takes iterations to refine content from a text article to a spoken dialogue, while considering visual compositions in each scene. We propose Doc2Video, a video prototyping approach that converts a document to interactive scripting with a preview of synthetic talking head videos. Our pipeline decomposes a source document into a series of scenes, each automatically creating a synthesized video of a virtual instructor. Designed for a specific domain -- programming cookbooks, we apply visual elements from the source document, such as a keyword, a code snippet or a screenshot, in suitable layouts. Users edit narration sentences, break or combine sections, and modify visuals to prototype a video in our Editing UI. We evaluated our pipeline with public programming cookbooks. Feedback from professional creators shows that our method provided a reasonable starting point to engage them in interactive scripting for a narrated instructional video.
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