SPDQ: Synergetic Prompts as Disentanglement Queries for Compositional Zero-Shot Learning

Compositional zero-shot learning (CZSL) aims to identify novel compositions formed by known primitives (attributes and objects). Motivated by recent advancements in pre-trained vision-language models such as CLIP, many methods attempt to fine-tune CLIP for CZSL and achieve remarkable performance. However, the existing CLIP-based CZSL methods focus mainly on text prompt tuning, which lacks the flexibility to dynamically adapt both modalities. To solve this issue, an intuitive solution is to additionally introduce visual prompt tuning. This insight is not trivial to achieve because effectively learning prompts for CZSL involves the challenge of entanglement between visual primitives as well as appearance shifts in different compositions. In this paper, we propose a novel Synergetic Prompts as Disentanglement Queries (SPDQ) framework for CZSL. It can disentangle primitive features based on synergetic prompts to jointly alleviate these challenges. Specifically, we first design a low-rank primitive modulator to produce synergetic adaptive attribute and object prompts based on prior knowledge of each instance for model adaptation. Then, we additionally utilize text prefix prompts to construct synergetic prompt queries, which are used to resample corresponding visual features from local visual patches. Comprehensive experiments conducted on three benchmarks demonstrate that our SPDQ approach achieves state-of-the-art results.