{"ID":2852090,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.18357","arxiv_id":"2510.18357","title":"Learning Human-Object Interaction as Groups","abstract":"Human-Object Interaction Detection (HOI-DET) aims to localize human-object pairs and identify their interactive relationships. To aggregate contextual cues, existing methods typically propagate information across all detected entities via self-attention mechanisms, or establish message passing between humans and objects with bipartite graphs. However, they primarily focus on pairwise relationships, overlooking that interactions in real-world scenarios often emerge from collective behaviors (multiple humans and objects engaging in joint activities). In light of this, we revisit relation modeling from a group view and propose GroupHOI, a framework that propagates contextual information in terms of geometric proximity and semantic similarity. To exploit the geometric proximity, humans and objects are grouped into distinct clusters using a learnable proximity estimator based on spatial features derived from bounding boxes. In each group, a soft correspondence is computed via self-attention to aggregate and dispatch contextual cues. To incorporate the semantic similarity, we enhance the vanilla transformer-based interaction decoder with local contextual cues from HO-pair features. Extensive experiments on HICO-DET and V-COCO benchmarks demonstrate the superiority of GroupHOI over the state-of-the-art methods. It also exhibits leading performance on the more challenging Nonverbal Interaction Detection (NVI-DET) task, which involves varied forms of higher-order interactions within groups.","short_abstract":"Human-Object Interaction Detection (HOI-DET) aims to localize human-object pairs and identify their interactive relationships. To aggregate contextual cues, existing methods typically propagate information across all detected entities via self-attention mechanisms, or establish message passing between humans and object...","url_abs":"https://arxiv.org/abs/2510.18357","url_pdf":"https://arxiv.org/pdf/2510.18357v1","authors":"[\"Jiajun Hong\",\"Jianan Wei\",\"Wenguan Wang\"]","published":"2025-10-21T07:25:10Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Transformer\"]","has_code":false}