Discovering a Variety of Objects in Spatio-Temporal Human-Object Interactions

• URL: http://arxiv.org/abs/2211.07501v1
• Date: Mon, 14 Nov 2022 16:33:54 GMT
• Title: Discovering a Variety of Objects in Spatio-Temporal Human-Object Interactions
• Authors: Yong-Lu Li, Hongwei Fan, Zuoyu Qiu, Yiming Dou, Liang Xu, Hao-Shu Fang, Peiyang Guo, Haisheng Su, Dongliang Wang, Wei Wu, Cewu Lu
• Abstract summary: In daily HOIs, humans often interact with a variety of objects, e.g., holding and touching dozens of household items in cleaning. Here, we introduce a new benchmark based on AVA: Discoveringed Objects (DIO) including 51 interactions and 1,000+ objects. An ST-HOI learning task is proposed expecting vision systems to track human actors, detect interactions and simultaneously discover objects.
• Score: 45.92485321148352
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spatio-temporal Human-Object Interaction (ST-HOI) detection aims at detecting HOIs from videos, which is crucial for activity understanding. In daily HOIs, humans often interact with a variety of objects, e.g., holding and touching dozens of household items in cleaning. However, existing whole body-object interaction video benchmarks usually provide limited object classes. Here, we introduce a new benchmark based on AVA: Discovering Interacted Objects (DIO) including 51 interactions and 1,000+ objects. Accordingly, an ST-HOI learning task is proposed expecting vision systems to track human actors, detect interactions and simultaneously discover interacted objects. Even though today's detectors/trackers excel in object detection/tracking tasks, they perform unsatisfied to localize diverse/unseen objects in DIO. This profoundly reveals the limitation of current vision systems and poses a great challenge. Thus, how to leverage spatio-temporal cues to address object discovery is explored, and a Hierarchical Probe Network (HPN) is devised to discover interacted objects utilizing hierarchical spatio-temporal human/context cues. In extensive experiments, HPN demonstrates impressive performance. Data and code are available at https://github.com/DirtyHarryLYL/HAKE-AVA.

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