[Submitted on 4 Sep 2026]
Title:A Framework for Egocentric and Exocentric Procedural Understanding via Temporal Segmentation and Semantic Abstraction
View a PDF of the paper titled A Framework for Egocentric and Exocentric Procedural Understanding via Temporal Segmentation and Semantic Abstraction, by Vivek Chavan and 1 other authors
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Abstract:Long-horizon ego/exo data contains rich procedural evidence, but are redundant, noisy, and costly to process or retain. We propose a compact framework that converts continuous multimodal workplace video into a structured Procedural State Memory, implemented as a Work Environment Model (WEM). Inspired by event segmentation theory, we detect boundaries using changes in visual context, location, motion, narration, gaze/object interaction, and optional exocentric workspace evidence, rather than fixed windows or visual novelty alone. Each segment is abstracted into an evidence-linked event card containing actor, interval, location, action, objects/tools, pre/post state, confidence, and provenance. These event cards incrementally update the WEM, enabling compact, auditable documentation and retrieval under on-premise privacy constraints. We instantiate the design with frozen DINOv2 and VJEPA-2 encoders and a local language model, and outline evaluation criteria for segmentation quality, memory compression, retrieval fidelity, and long-horizon QA.
Comments: Accepted for oral and poster presentation at the ACVR Workshop, ECCV 2026. Non-archival abstract; not published in the workshop proceedings. 8 pages, 1 figure
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2610.00069 [cs.CV]
(or arXiv:2610.00069v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.00069
arXiv-issued DOI via DataCite
Submission history
From: Vivek Chavan [view email] [v1] Fri, 4 Sep 2026 17:34:52 UTC (222 KB)
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