  Pretrained vision-language models, such as CLIP, have demonstrated strong
generalization capabilities, making them promising tools in the realm of
zero-shot visual recognition. Visual relation detection (VRD) is a typical task
that identifies relationship (or interaction) types between object pairs within
an image. However, naively utilizing CLIP with prevalent class-based prompts
for zero-shot VRD has several weaknesses, e.g., it struggles to distinguish
between different fine-grained relation types and it neglects essential spatial
information of two objects. To this end, we propose a novel method for
zero-shot VRD: RECODE, which solves RElation detection via COmposite
DEscription prompts. Specifically, RECODE first decomposes each predicate
category into subject, object, and spatial components. Then, it leverages large
language models (LLMs) to generate description-based prompts (or visual cues)
for each component. Different visual cues enhance the discriminability of
similar relation categories from different perspectives, which significantly
boosts performance in VRD. To dynamically fuse different cues, we further
introduce a chain-of-thought method that prompts LLMs to generate reasonable
weights for different visual cues. Extensive experiments on four VRD benchmarks
have demonstrated the effectiveness and interpretability of RECODE.
