{"slug": "dynamic-manipulation-hypergraphs-for-har-beyond-pairwise-relations-dynamic-for", "title": "Dynamic Manipulation Hypergraphs for HAR: Beyond Pairwise Relations: Dynamic Manipulation Hypergraphs for Vision-Based Human Activity Recognition", "summary": "Researchers propose a dynamic manipulation hypergraph framework for fine-grained human activity recognition that models multi-entity configurations as higher-order relational units, outperforming pairwise graph methods by 6.9 percentage points on EPIC-KITCHENS-100/VISOR and 9.5 points on Assembly101 in hand-object interaction F1 score.", "body_md": "arXiv:2607.14350v1 Announce Type: new\nAbstract: Fine-grained manipulation recognition requires modeling evolving relations among hands, objects, tools, and supporting surfaces. Conventional graph-based methods use pairwise edges that can fragment a coordinated event into disconnected binary relations. We propose a dynamic manipulation hypergraph framework that represents multi-entity configurations as higher-order relational units. At each temporal step, relevant entities are encoded using appearance, spatial, motion, and semantic-role features. Hyperedge candidates are instantiated and ranked using proximity, contact, and motion-coupling predicates. A hypergraph reasoning network performs node-to-hyperedge and hyperedge-to-node message passing, followed by temporal attention over the evolving interaction structure. The framework provides class-agnostic hyperedge-importance scores that identify entity configurations and temporal intervals emphasized by the model without treating them as causal explanations. Quantitative evaluation is conducted on EPIC-KITCHENS-100/VISOR and Assembly101 under an annotation-assisted entity-localization protocol. Video-only and entity-based methods provide contextual comparisons, while a matched pairwise graph and a static hypergraph serve as the principal controlled baselines because they use identical entity inputs and comparable relational settings. The proposed method improves HO-F1 over the matched pairwise graph by 6.9 percentage points on EPIC-KITCHENS-100/VISOR and 9.5 points on Assembly101, and exceeds the static hypergraph by 4.4 and 5.8 points, respectively. Qualitative analysis on ARCTIC further shows correspondence between highly ranked hyperedges and contact-rich manipulation intervals. These results demonstrate the value of time-varying higher-order relational modeling for fine-grained manipulation activity recognition.", "url": "https://wpnews.pro/news/dynamic-manipulation-hypergraphs-for-har-beyond-pairwise-relations-dynamic-for", "canonical_source": "https://arxiv.org/abs/2607.14350", "published_at": "2026-07-17 04:00:00+00:00", "updated_at": "2026-07-17 04:08:05.217314+00:00", "lang": "en", "topics": ["computer-vision", "machine-learning", "artificial-intelligence"], "entities": ["EPIC-KITCHENS-100", "VISOR", "Assembly101", "ARCTIC"], "alternates": {"html": "https://wpnews.pro/news/dynamic-manipulation-hypergraphs-for-har-beyond-pairwise-relations-dynamic-for", "markdown": "https://wpnews.pro/news/dynamic-manipulation-hypergraphs-for-har-beyond-pairwise-relations-dynamic-for.md", "text": "https://wpnews.pro/news/dynamic-manipulation-hypergraphs-for-har-beyond-pairwise-relations-dynamic-for.txt", "jsonld": "https://wpnews.pro/news/dynamic-manipulation-hypergraphs-for-har-beyond-pairwise-relations-dynamic-for.jsonld"}}