CARPRT: Class-Aware Zero-Shot Prompt Reweighting for Black-Box Vision-Language Models
Existing zero-shot image classification methods using vision-language models (VLMs) often employ a uniform weighting of prompts across all classes, ignoring the class-specific suitability of prompts. CARPRT introduces a training-free, class-aware reweighting scheme that adjusts the weight vector for each class based on the relevance of prompts to that class. Experiments show that CARPRT outperforms class-independent reweighting methods, highlighting the importance of modeling prompt-class dependencies.
-->
[Submitted on 19 Jun 2026]
Title:CARPRT: Class-Aware Zero-Shot Prompt Reweighting for Black-Box Vision-Language Models
View a PDF of the paper titled CARPRT: Class-Aware Zero-Shot Prompt Reweighting for Black-Box Vision-Language Models, by Ruijiang Dong and 5 other authors
View PDF HTML (experimental)
Abstract:Pre-trained vision-language models (VLMs) enable zero-shot image classification by computing the similarity score between an image and textual descriptions, typically formed by inserting a class label (e.g., "cat") into a prompt (e.g., "a photo of a"). Since the score for a given image-class pair is sensitive to the choice of prompt, existing studies ensemble multiple prompts using a weighting vector to aggregate scores across different prompts. Yet, in current strategies, the weighting vector assigned to each prompt is shared across all classes, implicitly assuming that prompts are conditionally independent of classes, which often does not hold in practice, as a prompt like "an aerial view of" might be apt for "airport" but ill-suited for "apple". To address this, we propose class-aware zero-shot prompt reweighting (CARPRT). This scoring scheme adjusts the weighting vector for each class label by capturing the class-specific relevance of different prompts in a training-free manner. For each class label and every available prompt, we quantify their class-specific relevance by averaging image-text relevance scores over images predicted to that class under the given prompt. These estimates are then normalized to derive class-specific weights. Evaluations on standard image classification benchmarks show that CARPRT outperforms existing class-independent reweighting methods, confirming that modeling prompt-class dependencies is crucial for effective zero-shot prediction and even broader VLM-based application settings that rely on prompt ensembling. Our code is available at this https URL.
Comments: Accepted at ICLR 2026
Subjects:
Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.14125 [cs.LG]
(or arXiv:2607.14125v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.14125
arXiv-issued DOI via DataCite
Submission history
From: Ruijiang Dong [view email] [v1] Fri, 19 Jun 2026 07:15:46 UTC (6,042 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled CARPRT: Class-Aware Zero-Shot Prompt Reweighting for Black-Box Vision-Language Models, by Ruijiang Dong and 5 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.LG
new | recent | 2026-07
Change to browse by:
cs cs.CV
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
IArxiv recommender toggle
IArxiv Recommender (What is IArxiv?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)