Exploring Self- and Cross-Triplet Correlations for Human-Object Interaction Detection
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Abstract (arXiv)
Human-Object Interaction (HOI) detection plays a vital role in scene understanding, which aims to predict the HOI triplet in the form of <human, object, action>. Existing methods mainly extract multi-modal features (e.g., appearance, object semantics, human pose) and then fuse them together to directly predict HOI triplets. However, most of these methods focus on seeking for self-triplet aggregation, but ignore the potential cross-triplet dependencies, resulting in ambiguity of action prediction. In this work, we propose to explore Self- and Cross-Triplet Correlations (SCTC) for HOI detection. Specifically, we regard each triplet proposal as a graph where Human, Object represent nodes and Action indicates edge, to aggregate self-triplet correlation. Also, we try to explore cross-triplet dependencies by jointly considering instance-level, semantic-level, and layout-level relations. Besides, we leverage the CLIP model to assist our SCTC obtain interaction-aware feature by knowledge distillation, which provides useful action clues for HOI detection. Extensive experiments on HICO-DET and V-COCO datasets verify the effectiveness of our proposed SCTC.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 32 of its 39 references that are in the map. First posted on arXiv 11 Jan 2024.
Builds on
32 of 39 references.
References · 32
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20 more
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Built on by
Citing papers per year: 2024: 1; 2025: 3; 2026: 3.
7 papers.
- 2025Open-Vocabulary HOI Detection with Interaction-aware Prompt and Concept Calibrationcited by 7
- 2025Learning Human-Object Interaction as Groupscited by 5
- 2024ContextHOI: Spatial Context Learning for Human-Object Interaction Detectioncited by 2
- 2025Mitigating Long-Tail Bias in HOI Detection via Adaptive Diversity Cachecited by 1
- 2026Mining Instance-Centric Vision-Language Contexts for Human-Object Interaction Detectioncited by 1
- 2026Unleashing Multimodal Large Language Models for Training-free HOI Detection in the Wildcited by 1
- 2026Denoise and Align: Diffusion-Driven Foreground Knowledge Prompting for Open-Vocabulary Temporal Action Detectioncited by 0
- 2026Unleashing Multimodal Large Language Models for Training-free HOI Detection in the Wild15 Jul 2026
- 2026Denoise and Align: Diffusion-Driven Foreground Knowledge Prompting for Open-Vocabulary Temporal Action Detection20 Apr 2026
- 2026Mining Instance-Centric Vision-Language Contexts for Human-Object Interaction Detection2 Apr 2026
- 2025Mitigating Long-Tail Bias in HOI Detection via Adaptive Diversity Cache24 Nov 2025
- 2025Learning Human-Object Interaction as Groups21 Oct 2025
- 2025Open-Vocabulary HOI Detection with Interaction-aware Prompt and Concept Calibration5 Aug 2025
- 2024ContextHOI: Spatial Context Learning for Human-Object Interaction Detection12 Dec 2024