Do Egocentric Video-Language Models Capture Both Hand- and Object-Centric Cues?
Hand-object interaction (HOI) recognition requires capturing both hand manipulations and object transformations. However, existing video-language models often fall into shortcuts by relying on spurious correlations among hands, objects, or environmental context, rather than reasoning from the appearance and dynamics of hands and objects themselves. To address this limitation, we propose a new learning paradigm that combines (i) hand-object masked training, which enables robust reasoning from partial hand or object observations, and (ii) an HOI-dynamics-aware decoder that explicitly learns hand- and object-centric embeddings through auxiliary predictions of their locations and semantics, e...
| Authors | Masatoshi Tateno, Alexandros Stergiou, Risa Shinoda et al. |
|---|---|
| Primary category | cs.CV |
| Published | 9 July 2026 |
| Last updated at source | 9 July 2026 |
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