HOICLIP: Efficient Knowledge Transfer for HOI Detection with Vision-Language Models

Understand

Human-Object Interaction (HOI) detection aims to localize human-object pairs and recognize their interactions.

• Recently, Contrastive Language-Image Pre-training (CLIP) has shown great potential in providing interaction prior for HOI detectors via knowledge distillation.
• However, such approaches often rely on large-scale training data and suffer from inferior performance under few/zero-shot scenarios.
• In this paper, we propose a novel HOI detection framework that efficiently extracts prior knowledge from CLIP and achieves better generalization.