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arXiv · 2508.01742

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

Qiaohui Chu·Haoyu Zhang·Meng Liu·Yisen Feng·Haoxiang Shi·Liqiang Nie
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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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Qiaohui Chu, Haoyu Zhang, Meng Liu, Yisen Feng, Haoxiang Shi, Liqiang Nie. 2025-08-03. Intention-Guided Cognitive Reasoning for Egocentric Long-Term Action Anticipation. https://arxiv.org/abs/2508.01742

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