Context Sensitive Network for weakly-supervised fine-grained temporal action localization.

Area of Science:

• Computer Vision
• Machine Learning
• Artificial Intelligence

Background:

• Weakly-supervised fine-grained temporal action localization (WS-FG-TAL) aims to detect precise action instances in videos using only high-level labels.
• Distinguishing between subtle, fine-grained action categories presents a significant challenge in WS-FG-TAL.
• Contextual information within videos is vital for improving WS-FG-TAL, but effectively integrating multi-scale context is complex.

Purpose of the Study:

• To propose a novel approach, the Context Sensitive Network (CSN), for enhancing WS-FG-TAL by effectively leveraging multi-scale context information.
• To address the challenge of adaptively selecting informative contexts across different scales for improved action localization.

Main Methods:

• Developed a multi-scale context extraction module to capture temporal contexts at various scales.
• Introduced a scale-sensitive context gating module to enable interaction among multi-scale contexts and adaptively select the most informative ones.
• Evaluated the proposed approach on the FineGym and FineAction benchmark datasets.

Main Results:

• The Context Sensitive Network achieved state-of-the-art performance on both benchmark datasets.
• Demonstrated the effectiveness of the multi-scale context extraction and scale-sensitive gating modules in improving WS-FG-TAL.

Conclusions:

• Leveraging multi-scale context information is crucial for accurate weakly-supervised fine-grained temporal action localization.
• The proposed Context Sensitive Network effectively integrates and adaptively selects context information, leading to superior performance in WS-FG-TAL.