Cross-view action recognition understanding from exocentric to egocentric perspective

• Published in: Neurocomputing (Amsterdam) 2025-01, Vol.614, p.128731, Article 128731
• Main Authors: Truong, Thanh-Dat, Luu, Khoa
• Format: Article
• Language: English
• Subjects: Cross-view action recognition; Egocentric action recognition; Self-attention
• ISSN: 0925-2312
• DOI: 10.1016/j.neucom.2024.128731

Understanding action recognition in egocentric videos has emerged as a vital research topic with numerous practical applications. With the limitation in the scale of egocentric data collection, learning robust deep learning-based action recognition models remains difficult. Transferring knowledge learned from the large-scale exocentric data to the egocentric data is challenging due to the difference in videos across views. Our work introduces a novel cross-view learning approach to action recognition (CVAR) that effectively transfers knowledge from the exocentric to the selfish view. First, we present a novel geometric-based constraint into the self-attention mechanism in Transformer based on analyzing the camera positions between two views. Then, we propose a new cross-view self-attention loss learned on unpaired cross-view data to enforce the self-attention mechanism learning to transfer knowledge across views. Finally, to further improve the performance of our cross-view learning approach, we present the metrics to measure the correlations in videos and attention maps effectively. Experimental results on standard egocentric action recognition benchmarks, i.e., Charades-Ego, EPIC-Kitchens-55, and EPIC-Kitchens-100, have shown our approach’s effectiveness and state-of-the-art performance.