Conditional Prompt Learning for Vision-Language Models

Kaiyang Zhou, Jingkang Yang, Chen Change Loy, Ziwei Liu*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

363 Citations (Scopus)

Abstract

With the rise of powerful pre-trained vision-language models like CLIP, it becomes essential to investigate ways to adapt these models to downstream datasets. A recently proposed method named Context Optimization (CoOp) introduces the concept of prompt learning - a recent trend in NLP - to the vision domain for adapting pre-trained vision-language models. Specifically, CoOp turns context words in a prompt into a set of learnable vectors and, with only a few labeled images for learning, can achieve huge improvements over intensively-tuned manual prompts. In our study we identify a critical problem of CoOp: the learned context is not generalizable to wider unseen classes within the same dataset, suggesting that CoOp overfits base classes observed during training. To address the problem, we propose Conditional Context Optimization (CoCoOp), which extends CoOp by further learning a lightweight neural network to generate for each image an input-conditional token (vector). Compared to CoOp's static prompts, our dynamic prompts adapt to each instance and are thus less sensitive to class shift. Extensive experiments show that CoCoOp generalizes much better than CoOp to unseen classes, even showing promising transferability beyond a single dataset; and yields stronger domain generalization performance as well. Code is available at https://github.com/KaiyangZhou/CoOp.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
PublisherIEEE
Pages16795-16804
Number of pages10
ISBN (Electronic)9781665469463
ISBN (Print)9781665469470
DOIs
Publication statusPublished - Jun 2022
Event35th IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022 - New Orleans, United States
Duration: 18 Jun 202224 Jun 2022
https://ieeexplore.ieee.org/xpl/conhome/9878378/proceeding

Publication series

NameProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Volume2022-June
ISSN (Print)1063-6919
ISSN (Electronic)2575-7075

Conference

Conference35th IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
Country/TerritoryUnited States
CityNew Orleans
Period18/06/2224/06/22
Internet address

Scopus Subject Areas

  • Software
  • Computer Vision and Pattern Recognition

User-Defined Keywords

  • Representation learning

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