LINK: Learning Instance-level Knowledge from Vision-Language Models for Human-Object Interaction Detection

WU EASTMAN Z Y, Ya-Li Li, Yuan Wang, Shengjin Wang

Keywords: Human-object interaction detection, Vision-language model, zero-shot, open-vocabulary

Abstract:

Human-Object Interaction (HOI) detection with vision-language models (VLMs) has progressed rapidly, yet a trade-off persists between specialization and generalization.
Two major challenges remain: (1) the sparsity of supervision, which hampers effective transfer of foundation models to HOI tasks, and (2) the absence of a generalizable architecture that can excel in both fully supervised and zero-shot scenarios.
To address these issues, we propose \textbf{LINK}, \textbf{L}earning \textbf{IN}stance-level \textbf{K}nowledge from VLMs.
First, we introduce a HOI detection framework equipped with a Human-Object Geometrical Encoder and a VLM Linking Decoder.
By decoupling from detector-specific features, our design ensures plug-and-play compatibility with arbitrary object detectors and consistent adaptability across diverse settings.
Building on this foundation, we develop a Progressive Learning Strategy under a teacher-student paradigm, which delivers dense supervision over all potential human-object pairs.
By contrasting subtle spatial and semantic differences between positive and negative instances, the model learns robust and transferable HOI representations.
LINK sets new state-of-the-art on SWiG-HOI, HICO-DET, and V-COCO across zero-shot, fully supervised, and open-vocabulary settings, with strong generalization to unseen interaction categories.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

Submission Number: 15124