[Submitted on 4 Nov 2026]
Title:TraCE: Trajectory-Aware Concepts for Explainable Video Understanding
View a PDF of the paper titled TraCE: Trajectory-Aware Concepts for Explainable Video Understanding, by Wooil Lee and 5 other authors
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Abstract:Video understanding models often rely on motion, yet their explanations can obscure which motion sources support a prediction. We identify motion-source under-specification as a limitation of existing video XAI: region-centric explanations can be source-ambiguous, while motion vocabularies restricted to single-person pose sequences are source-incomplete. We propose TraCE (Trajectory-Aware Concepts for Explainable Video Understanding), a concept-based framework that discovers source-flexible motion concepts by clustering optical-flow trajectories from training videos. These concepts represent dynamic evidence from people, objects, and interactions, while object and scene concepts account for static context. The resulting typed trajectory/object/scene interface supports class prediction for action recognition and question-conditioned concept matching for video question answering (VQA). We evaluate TraCE across five random seeds on six benchmarks: NTU RGB+D Mutual, Chiral SSv2, UCF-101, KTH, TOMATO, and STAR. Relative to non-interpretable baselines, TraCE maintains competitive task performance, with an average accuracy drop of 1.0 points for action recognition and a nominal gain of 0.4 points for VQA. Motion perturbation and concept-removal tests provide complementary evidence of directional semantic alignment and decision faithfulness, while sample-level interventions illustrate targeted model debugging. Our project page is available at this https URL.
Comments: Accepted to NeurIPS 2026. Project page: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2611.07009 [cs.CV]
(or arXiv:2611.07009v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2611.07009
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Wooil Lee [view email] [v1] Wed, 4 Nov 2026 19:00:01 UTC (4,971 KB)
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