Paper in IEEE WACV (2017): "Complex Event Recognition from Images with Few Training Examples"


Unaiza Ahsan, Chen Sun, James Hays, Irfan Essa

Complex Event Recognition from Images with Few Training Examples Proceedings Article

In: IEEE Winter Conference on Applications of Computer Vision (WACV), 2017.

Abstract | Links | BibTeX | Tags: activity recognition, computer vision, machine learning, WACV


We propose to leverage concept-level representations for complex event recognition in photographs given limited training examples. We introduce a novel framework to discover event concept attributes from the web and use that to extract semantic features from images and classify them into social event categories with few training examples. Discovered concepts include a variety of objects, scenes, actions and event subtypes, leading to a discriminative and compact representation for event images. Web images are obtained for each discovered event concept and we use (pre-trained) CNN features to train concept classifiers. Extensive experiments on challenging event datasets demonstrate that our proposed method outperforms several baselines using deep CNN features directly in classifying images into events with limited training examples. We also demonstrate that our method achieves the best overall accuracy on a data set with unseen event categories using a single training example.

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