[Submitted on 28 Sep 2026]
Title:HEIR: Learning Human-Entity Interactions with Functional Roles
View a PDF of the paper titled HEIR: Learning Human-Entity Interactions with Functional Roles, by Di Wen and 17 other authors
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Abstract:Understanding human-entity interactions requires recovering each person-action event's participants, roles, and shared identities. This structure can support embodied agents by clarifying who acts on which entities and how, informing anticipation and coordination in shared environments. Standard HOI metrics score individual links, leaving complete event composition undermeasured. We introduce HEIR (Human-Entity Interactions with Functional Roles), an image benchmark for complete grounded participant-role sets across object, interpersonal, and self-directed interactions. It contains 18,730 images, six roles, 105 actions, and 437 nouns, with shared entities, role changes, and repeated fillers; 51.6% of images contain multiple actors and 62.1% contain multiple actions. HEIR pairs relation AP with complete-set AP and structural evaluation. We also introduce CoRISP (Compositional Role-aware Interaction Set Prediction), which uses shared entity identities to combine role-conditioned evidence and predict normalized participant-role sets. Cardinality and role-multiplicity potentials couple assignments through event size and role composition, with exact per-event normalization. Across 16 baselines, relation and complete-event rankings diverge even after aligning action weights. CoRISP leads the evaluated systems on repeated-role events and shared-participant images in HEIR by 2.87 and 3.82 Set mAP points, respectively. On V-COCO, CoRISP achieves 73.72/76.23 role AP and 61.06/68.59 complete-set AP on two-slot actions under Scenarios 1/2. These results show the value of learning and evaluating event composition alongside individual relations. The code and dataset are publicly available at this https URL.
Comments: 24 pages, 4 figures. Code and dataset: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.35955 [cs.CV]
(or arXiv:2609.35955v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.35955
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Di Wen [view email] [v1] Mon, 28 Sep 2026 17:59:08 UTC (1,035 KB)
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