Reliable Egocentric Action Anticipation via Temporal Reliability Suppression and Compositional Graph Decoding

The paper presents a lightweight framework for reliable egocentric action anticipation that tackles two failure modes caused by temporal corruption: unreliable temporal evidence during encoding and implausible verb‑noun compositions during decoding. It introduces Temporal Reliability Suppression (TRS), which predicts per‑frame suppression scores to penalize encoder attention and weight temporal pooling, and Robust Verb‑Noun Graph (RVG), which re‑ranks verb‑noun pairs using a PMI‑based compatibility graph derived from training labels. Experiments with corruption‑augmented training show that TRS+CA+RVG achieves 29.1% average corrupted accuracy and 88.2% relative robustness across six corruptions, while dramatically reducing rare verb‑noun predictions from 15.4% to 1.1%. whyItMatters":"The approach improves the robustness of wearable action anticipation systems to real‑world challenges such as missing frames, masking, and sensor noise, which are common in egocentric video settings."

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