Papers › VSGNet: Spatial Attention Network for Detecting Human Object Interactions Using Graph...
VSGNet: Spatial Attention Network for Detecting Human Object Interactions Using Graph Convolutions
Oytun Ulutan, A. S. M. Iftekhar, B. S. Manjunath
Comprehensive visual understanding requires detection frameworks that can effectively learn and utilize object interactions while analyzing objects individually. This is the main objective in Human-Object Interaction (HOI) detection task. In particular, relative spatial reasoning and structural connections between objects are essential cues for analyzing interactions, which is addressed by the proposed Visual-Spatial-Graph Network (VSGNet) architecture. VSGNet extracts visual features from the human-object pairs, refines the features with spatial configurations of the pair, and utilizes the structural connections between the pair via graph convolutions. The performance of VSGNet is thoroughly evaluated using the Verbs in COCO (V-COCO) and HICO-DET datasets. Experimental results indicate that VSGNet outperforms state-of-the-art solutions by 8% or 4 mAP in V-COCO and 16% or 3 mAP in HICO-DET.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Human-Object Interaction Detection | HICO-DET | VSGNet | mAP | 19.8 | #50 of 55 | Archive leaderboard | report |
| Human-Object Interaction Detection | V-COCO | VSGNet | AP(S1) | 51.76 | #24 of 34 | Archive leaderboard | report |
| Human-Object Interaction Detection | V-COCO | VSGNet | AP(S2) | 57.0 | #24 of 34 | Archive leaderboard | report |
| Human-Object Interaction Detection | V-COCO | VSGNet | Time Per Frame(ms) | 312 | #24 of 34 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: VSGNet
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