  Compositional zero-shot learning (CZSL) task aims to recognize unseen
compositional visual concepts, e.g., sliced tomatoes, where the model is
learned only from the seen compositions, e.g., sliced potatoes and red
tomatoes. Thanks to the prompt tuning on large pre-trained visual language
models such as CLIP, recent literature shows impressively better CZSL
performance than traditional vision-based methods. However, the key aspects
that impact the generalization to unseen compositions, including the diversity
and informativeness of class context, and the entanglement between visual
primitives, i.e., state and object, are not properly addressed in existing
CLIP-based CZSL literature. In this paper, we propose a model by prompting the
language-informed distribution, aka., PLID, for the CZSL task. Specifically,
the PLID leverages pre-trained large language models (LLM) to (i) formulate the
language-informed class distributions which are diverse and informative, and
(ii) enhance the compositionality of the class embedding. Moreover, a
visual-language primitive decomposition (VLPD) module is proposed to
dynamically fuse the classification decisions from the compositional and the
primitive space. Orthogonal to the existing literature of soft, hard, or
distributional prompts, our method advocates prompting the LLM-supported class
distributions, leading to a better zero-shot generalization. Experimental
results on MIT-States, UT-Zappos, and C-GQA datasets show the superior
performance of the PLID to the prior arts. Our code and models are released:
https://github.com/Cogito2012/PLID.
