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arXiv Computer Vision By Ryoya Yoshiyama, Bowen Wang, Yuta Nakashima

Drop the Details, Get the Gist: Dynamic Conceptual Representation for Video Recognition

Read the original on arXiv Computer Vision →

The paper introduces a dynamic conceptual representation framework for video recognition, extending Concept Bottleneck Models from static images to procedural videos. It encodes a multi‑step video as a temporal sequence of segment‑level concept score vectors and processes this sequence with a Transformer encoder to predict the video’s class label. Experiments on cooking and household‑activity videos show that coarser‑grained concepts are sufficient for accurate classification, suggesting that fine‑grained textual descriptions may be unnecessary.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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