Discovering A Variety of Objects in Spatio-Temporal Human-Object Interactions
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Abstract (arXiv)
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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 63 of its 83 references that are in the map. First posted on arXiv 14 Nov 2022.
Builds on
63 of 83 references.
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Citing papers per year: 2022: 1; 2023: 2; 2024: 2; 2025: 0; 2026: 1.
6 papers.
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- 2023From Isolated Islands to Pangea: Unifying Semantic Space for Human Action Understandingcited by 19
- 2022Beyond Object Recognition: A New Benchmark towards Object Concept Learningcited by 11
- 2023Diagnosing Human-object Interaction Detectorscited by 11
- 2024Simultaneous Detection and Interaction Reasoning for Object-Centric Action Recognitioncited by 8
- 2026WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory and In-Context Learningcited by 4
- 2026WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory and In-Context Learning21 Jul 2026
- 2024Simultaneous Detection and Interaction Reasoning for Object-Centric Action Recognition18 Apr 2024
- 2024Robo-ABC: Affordance Generalization Beyond Categories via Semantic Correspondence for Robot Manipulation15 Jan 2024
- 2023Diagnosing Human-object Interaction Detectors16 Aug 2023
- 2023From Isolated Islands to Pangea: Unifying Semantic Space for Human Action Understanding2 Apr 2023
- 2022Beyond Object Recognition: A New Benchmark towards Object Concept Learning6 Dec 2022