Semi-supervised Active Learning for Video Action Detection

• URL: http://arxiv.org/abs/2312.07169v3
• Date: Wed, 3 Apr 2024 15:11:33 GMT
• Title: Semi-supervised Active Learning for Video Action Detection
• Authors: Ayush Singh, Aayush J Rana, Akash Kumar, Shruti Vyas, Yogesh Singh Rawat,
• Abstract summary: We develop a novel semi-supervised active learning approach which utilizes both labeled as well as unlabeled data. We evaluate the proposed approach on three different benchmark datasets, UCF-24-101, JHMDB-21, and Youtube-VOS.
• Score: 8.110693267550346
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we focus on label efficient learning for video action detection. We develop a novel semi-supervised active learning approach which utilizes both labeled as well as unlabeled data along with informative sample selection for action detection. Video action detection requires spatio-temporal localization along with classification, which poses several challenges for both active learning informative sample selection as well as semi-supervised learning pseudo label generation. First, we propose NoiseAug, a simple augmentation strategy which effectively selects informative samples for video action detection. Next, we propose fft-attention, a novel technique based on high-pass filtering which enables effective utilization of pseudo label for SSL in video action detection by emphasizing on relevant activity region within a video. We evaluate the proposed approach on three different benchmark datasets, UCF-101-24, JHMDB-21, and Youtube-VOS. First, we demonstrate its effectiveness on video action detection where the proposed approach outperforms prior works in semi-supervised and weakly-supervised learning along with several baseline approaches in both UCF101-24 and JHMDB-21. Next, we also show its effectiveness on Youtube-VOS for video object segmentation demonstrating its generalization capability for other dense prediction tasks in videos. The code and models is publicly available at: \url{https://github.com/AKASH2907/semi-sup-active-learning}.

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