Abstract
Classification of a new class entities requires collecting and annotating hundreds or thousands of samples that is often prohibitively time consuming and costly. Few-shot learning (FSL) suggests learning to classify new classes using just a few examples. Only a small number of studies address the challenge of using just a few labeled samples to learn a new spatio-temporal pattern such as videos. In this paper, we present a Temporal Aware Embedding Network (TAEN) for few-shot action recognition, that learns to represent actions, in a metric space as a trajectory, conveying both short term semantics and longer term connectivity between sub-actions. We demonstrate the effectiveness of TAEN on two few shot tasks, video classification and temporal action detection. We achieve state-of-the-art results on the Kinetics few-shot benchmark and on the ActivityNet 1.2 few-shot temporal action detection task. Code will be released upon acceptance of the paper.
Abstract (translated)
URL
https://arxiv.org/abs/2004.10141
