Few-Shot Action Localization without Knowing Boundaries

Abstract

Learning to localize actions in long, cluttered, and untrimmed videos is a hard task, that in the literature has typically been addressed assuming the availability of large amounts of annotated training samples for each class -either in a fully-supervised setting, where action boundaries are known, or in a weakly-supervised setting, where only class labels are known for each video. In this paper, we go a step further and show that it is possible to learn to localize actions in untrimmed videos when a) only one/few trimmed examples of the target action are available at test time, and b) when a large collection of videos with only class label annotation (some trimmed and some weakly annotated untrimmed ones) are available for training; with no overlap between the classes used during training and testing. To do so, we propose a network that learns to estimate Temporal Similarity Matrices (TSMs) that model a finegrained similarity pattern between pairs of videos (trimmed or untrimmed), and uses them to generate Temporal Class Activation Maps (TCAMs) for seen or unseen classes. The TCAMs serve as temporal attention mechanisms to extract video-level representations of untrimmed videos, and to temporally localize actions at test time. To the best of our knowledge, we are the first to propose a weakly-supervised, one/few-shot action localization network that can be trained in an end-to-end fashion. Experimental results on THUMOS14 and ActivityNet1.2 datasets, show that our method achieves performance comparable or better to state-of-the-art fullysupervised, few-shot learning methods. CCS CONCEPTS• Computing methodologies → Machine learning.

Human Action Recognition From Various Data Modalities: A Review

“…It can be difficult to collect a large amount of training data (especially multi-modality data) for all action classes. To handle this issue, one of the possible solutions is to take advantage of few-shot learning techniques [498], [499]. Though there have been some attempts for few-shot HAR [100], [185], considering the significance of handling the issues of scarcity of data in many practical scenarios, more advanced few-shot action analysis can still be further explored.…”