Revisiting Few-shot Activity Detection with Class Similarity Control
This addresses the problem of detecting rare events in videos for applications like surveillance or content analysis, but it is incremental as it builds on existing few-shot detection methods.
The paper tackles few-shot temporal activity detection in untrimmed videos by proposing a novel framework based on proposal regression, achieving competitive performance on large-scale benchmarks like ActivityNet1.2, ActivityNet1.3, and THUMOS14.
Many interesting events in the real world are rare making preannotated machine learning ready videos a rarity in consequence. Thus, temporal activity detection models that are able to learn from a few examples are desirable. In this paper, we present a conceptually simple and general yet novel framework for few-shot temporal activity detection based on proposal regression which detects the start and end time of the activities in untrimmed videos. Our model is end-to-end trainable, takes into account the frame rate differences between few-shot activities and untrimmed test videos, and can benefit from additional few-shot examples. We experiment on three large scale benchmarks for temporal activity detection (ActivityNet1.2, ActivityNet1.3 and THUMOS14 datasets) in a few-shot setting. We also study the effect on performance of different amount of overlap with activities used to pretrain the video classification backbone and propose corrective measures for future works in this domain. Our code will be made available.