Troika: Multi-Path Cross-Modal Traction for Compositional Zero-Shot Learning

• Published in: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp. 24005 - 24014
• Main Authors: Huang, Siteng, Gong, Biao, Feng, Yutong, Zhang, Min, Lv, Yiliang, Wang, Donglin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 16.06.2024
• Subjects: Adaptation models; Benchmark testing; Codes; compositional zero-shot learning; Computer vision; Limiting; parameter-efficient transfer learning; Troika; vision-language models; Visualization; Zero-shot learning
• ISSN: 2575-7075
• DOI: 10.1109/CVPR52733.2024.02266

Recent compositional zero-shot learning (CZSL) methods adapt pre-trained vision-language models (VLMs) by constructing trainable prompts only for composed state-object pairs. Relying on learning the joint representation of seen compositions, these methods ignore the explicit modeling of the state and object, thus limiting the exploitation of pre-trained knowledge and generalization to unseen compositions. With a particular focus on the universality of the solution, in this work, we propose a novel paradigm for CZSL models that establishes three identification branches (i.e., Multi-Path) to jointly model the state, object, and composition. The presented Troika is an outstanding implementation that aligns the branch-specific prompt representations with decomposed visual features. To calibrate the bias between semantically similar multi-modal representations, we further devise a Cross-Modal Traction module into Troika that shifts the prompt representation towards the current visual content. We conduct extensive experiments on three popular benchmarks, where our method significantly outperforms existing methods in both closed-world and open-world settings. The code will be available at https://github.com/bighuang624/Troika.