Disentangled Concepts Speak Louder Than Words: Explainable Video Action Recognition

TL;DR

DANCE is a novel framework for video action recognition that disentangles motion, objects, and scenes to provide clearer explanations and improve interpretability without sacrificing accuracy.

Contribution

It introduces a concept-based approach using language models and an ante-hoc design to enhance explainability in video action recognition.

Findings

Significantly improves explanation clarity.

Achieves competitive recognition performance.

Enhances model debugging and failure analysis.

Abstract

Effective explanations of video action recognition models should disentangle how movements unfold over time from the surrounding spatial context. However, existing methods based on saliency produce entangled explanations, making it unclear whether predictions rely on motion or spatial context. Language-based approaches offer structure but often fail to explain motions due to their tacit nature -- intuitively understood but difficult to verbalize. To address these challenges, we propose Disentangled Action aNd Context concept-based Explainable (DANCE) video action recognition, a framework that predicts actions through disentangled concept types: motion dynamics, objects, and scenes. We define motion dynamics concepts as human pose sequences. We employ a large language model to automatically extract object and scene concepts. Built on an ante-hoc concept bottleneck design, DANCE enforces prediction through these concepts. Experiments on four datasets -- KTH, Penn Action, HAA500, and UCF-101 -- demonstrate that DANCE significantly improves explanation clarity with competitive performance. We validate the superior interpretability of DANCE through a user study. Experimental results also show that DANCE is beneficial for model debugging, editing, and failure analysis.