  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
our 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.
