  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.
