Papers › Detecting and Recognizing Human-Object Interactions

Detecting and Recognizing Human-Object Interactions

24 Apr 2017CVPR 2018 6arXiv:1704.07333archive 2025-07-28

Georgia Gkioxari, Ross Girshick, Piotr Dollár, Kaiming He

To understand the visual world, a machine must not only recognize individual object instances but also how they interact. Humans are often at the center of such interactions and detecting human-object interactions is an important practical and scientific problem. In this paper, we address the task of detecting <human, verb, object> triplets in challenging everyday photos. We propose a novel model that is driven by a human-centric approach. Our hypothesis is that the appearance of a person -- their pose, clothing, action -- is a powerful cue for localizing the objects they are interacting with. To exploit this cue, our model learns to predict an action-specific density over target object locations based on the appearance of a detected person. Our model also jointly learns to detect people and objects, and by fusing these predictions it efficiently infers interaction triplets in a clean, jointly trained end-to-end system we call InteractNet. We validate our approach on the recently introduced Verbs in COCO (V-COCO) and HICO-DET datasets, where we show quantitatively compelling results.

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facebookresearch/detectron mentioned on GitHubpytorch report
jiajunhua/facebookresearch-Detectron mentioned on GitHubcaffe2 report

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Human-Object Interaction DetectionObject

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Human-Object Interaction Detection HICO-DET InteractNet Time Per Frame (ms) 145 #55 of 55 Archive leaderboard report
Human-Object Interaction Detection HICO-DET InteractNet mAP 9.94 #55 of 55 Archive leaderboard report

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