Exploring the Potential of Large Foundation Models for Open-Vocabulary HOI Detection
Authors: Ting Lei, Shaofeng Yin, Yang Liu

Open-vocabulary human-object interaction (HOI) detection which is concerned with the problem of detecting novel HOIs guided by natural language is crucial for understanding human-centric scenes. However prior zero-shot HOI detectors often employ the same levels of feature maps to model HOIs with varying distances leading to suboptimal performance in scenes containing human-object pairs with a wide range of distances. In addition these detectors primarily rely on category names and overlook the rich contextual information that language can provide which is essential for capturing open vocabulary concepts that are typically rare and not well-represented by category names alone. In this paper we introduce a novel end-to-end open vocabulary HOI detection framework with conditional multi-level decoding and fine-grained semantic enhancement (CMD-SE) harnessing the potential of Visual-Language Models (VLMs). Specifically we propose to model human-object pairs with different distances with different levels of feature maps by incorporating a soft constraint during the bipartite matching process. Furthermore by leveraging large language models (LLMs) such as GPT models we exploit their extensive world knowledge to generate descriptions of human body part states for various interactions. Then we integrate the generalizable and fine-grained semantics of human body parts to improve interaction recognition. Experimental results on two datasets SWIG-HOI and HICO-DET demonstrate that our proposed method achieves state-of-the-art results in open vocabulary HOI detection. The code and models are available at https://github.com/ltttpku/CMD-SE-release.