Intention-Guided Cognitive Reasoning for Egocentric Long-Term Action Anticipation

• URL: http://arxiv.org/abs/2508.01742v1
• Date: Sun, 03 Aug 2025 12:52:27 GMT
• Title: Intention-Guided Cognitive Reasoning for Egocentric Long-Term Action Anticipation
• Authors: Qiaohui Chu, Haoyu Zhang, Meng Liu, Yisen Feng, Haoxiang Shi, Liqiang Nie,
• Abstract summary: INSIGHT is a two-stage framework for egocentric action anticipation.<n>In the first stage, INSIGHT focuses on extracting semantically rich features from hand-object interaction regions.<n>In the second stage, it introduces a reinforcement learning-based module that simulates explicit cognitive reasoning.
• Score: 52.6091162517921
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Long-term action anticipation from egocentric video is critical for applications such as human-computer interaction and assistive technologies, where anticipating user intent enables proactive and context-aware AI assistance. However, existing approaches suffer from three key limitations: 1) underutilization of fine-grained visual cues from hand-object interactions, 2) neglect of semantic dependencies between verbs and nouns, and 3) lack of explicit cognitive reasoning, limiting generalization and long-term forecasting ability. To overcome these challenges, we propose INSIGHT, a unified two-stage framework for egocentric action anticipation. In the first stage, INSIGHT focuses on extracting semantically rich features from hand-object interaction regions and enhances action representations using a verb-noun co-occurrence matrix. In the second stage, it introduces a reinforcement learning-based module that simulates explicit cognitive reasoning through a structured process: visual perception (think) -> intention inference (reason) -> action anticipation (answer). Extensive experiments on Ego4D, EPIC-Kitchens-55, and EGTEA Gaze+ benchmarks show that INSIGHT achieves state-of-the-art performance, demonstrating its effectiveness and strong generalization capability.

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