mlsanityData to 7 Oct 2026

Video-based Human-Object Interaction Detection from Tubelet Tokens

arXiv 2206.01908 ↗First posted 4 Jun 2022cs.CVcited by 13

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Abstract (arXiv)

We present a novel vision Transformer, named TUTOR, which is able to learn tubelet tokens, served as highly-abstracted spatiotemporal representations, for video-based human-object interaction (V-HOI) detection. The tubelet tokens structurize videos by agglomerating and linking semantically-related patch tokens along spatial and temporal domains, which enjoy two benefits: 1) Compactness: each tubelet token is learned by a selective attention mechanism to reduce redundant spatial dependencies from others; 2) Expressiveness: each tubelet token is enabled to align with a semantic instance, i.e., an object or a human, across frames, thanks to agglomeration and linking. The effectiveness and efficiency of TUTOR are verified by extensive experiments. Results shows our method outperforms existing works by large margins, with a relative mAP gain of 16.14% on VidHOI and a 2 points gain on CAD-120 as well as a 4 × speedup.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 44 of its 61 references that are in the map. First posted on arXiv 4 Jun 2022.

Builds on

44 of 61 references.

References · 44

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32 more
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Citing papers per year: 2023: 5; 2024: 3; 2025: 2; 2026: 3.

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