Hugging Face Trending Papers

Does Marginal Coverage Guarantee Class-Conditional Safety for Zero-Shot VLMs Under Shift?

arXiv AI

Aug 21

Does Marginal Coverage Guarantee Class-Conditional Safety for Zero-Shot VLMs Under Shift?

arXiv:2608. 19376v1 Announce Type: cross Abstract: Split-conformal prediction provides marginal coverage under exchangeability and is increasingly used as an abstention layer for zero-shot vision-language models (VLMs).

By Jai Kumar Sharma, Amartya Dutta

llms multimodal safety

arXiv Machine Learning

Jul 21

The Label Complexity of Class-Conditional Coverage under Distribution Shift

arXiv:2607. 18088v1 Announce Type: new Abstract: Standard evaluation of many recognition systems contains distribution shift by construction, since benchmarks place disjoint conditions in the training and test splits.

By Weijia Han, Lisha Qu

benchmarks

arXiv Machine Learning

Aug 3

Mitigating Class-Tail Undercoverage in Medical Vision-Language Models under Clinical Shift

arXiv:2607. 28696v1 Announce Type: new Abstract: Medical vision-language models (VLMs) can retain high observed marginal coverage after clinical shift while substantially under-covering an individual disease class.

By Mushir Akhtar, M. Tanveer

llms multimodal

arXiv Computer Vision

Sep 1

GATE: Reliability-Gated Gaussian Evidence Fusion for Training-Free Test-Time Adaptation of Vision-Language Models

arXiv:2608.29395v1 Announce Type: new Abstract: Vision-language models such as CLIP and SigLIP provide strong zero-shot recognition, but their predictions can degrade when deployed on target data tha...

By Pedram MohajerAnsari, Amir Salarpour, Run Wang, Mert D. Pes\'e

llms multimodal benchmarks

arXiv Computer Vision

Sep 10

Learning to Adapt and Calibrate: Score Distribution Alignment for Few-Shot Uncertainty Prediction in Medical VLMs

arXiv:2609.10333v1 Announce Type: new Abstract: Uncertainty estimation for medical vision--language models (VLMs) using conformal prediction has gained increasing attention due to its distribution-fr...

By Xuan Cuong Ngo, Ngan Le

llms fine-tuning multimodal safety

arXiv AI

Jun 10

A Controlled Audit of Pretraining Contamination in Public Medical Vision-Language Benchmarks

arXiv:2606. 10066v1 Announce Type: cross Abstract: Medical vision-language models (VLMs) are evaluated on public benchmarks whose images and question-answer pairs have been freely downloadable for years, yet reported accuracy assumes these examples were absent from pretraining.

By Bruce Changlong Xu, Lan Wu, Alexander Ryu

llms multimodal benchmarks

arXiv Machine Learning

Jun 30

Conformal Prediction with Macro-Coverage Guarantees

arXiv:2606. 28598v1 Announce Type: cross Abstract: Prediction sets should have high coverage to be useful, but some coverage notions are more practically relevant than others.

By Aabesh Bhattacharyya, Tiffany Ding, Rina Foygel Barber

computer-vision

Hugging Face Trending Papers

Sep 9

Learning to Adapt and Calibrate: Score Distribution Alignment for Few-Shot Uncertainty Prediction in Medical VLMs

Uncertainty estimation for medical vision--language models (VLMs) using conformal prediction has gained increasing attention due to its distribution-free coverage guarantees. However, standard conform...

llms fine-tuning multimodal safety

arXiv AI

Jul 22

Attributes Should Come from Images, Not Class Names: Distribution-Conditioned Attribute Selection for Vision-Language Models

arXiv:2607. 18695v1 Announce Type: cross Abstract: A popular route to interpretable zero-shot classification asks a large language model (LLM) to describe each class name and prompts CLIP with the resulting descriptors.

By Gautam Rajendrakumar Gare, Jia Shi, Zhiqiu Lin, Deepak Pathak, John Galeotti, Deva Ramanan

llms rag multimodal

arXiv Computer Vision

Sep 10

Vision-language models know more about agriculture than they show and rubric-grounded verifications close the gap

arXiv:2609.09417v1 Announce Type: new Abstract: Vision-language models (VLMs) show promise for agricultural classification, but zero-shot performance on disease, pest, damage, quality, and species id...

By Earl Ranario, Jared Smith, Lars Lundqvist, Urmil Jatin Chandarana

llms multimodal benchmarks

arXiv Machine Learning

Jul 17

CARPRT: Class-Aware Zero-Shot Prompt Reweighting for Black-Box Vision-Language Models

arXiv:2607. 14125v1 Announce Type: new 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.

By Ruijiang Dong, Zesheng Ye, Jianzhong Qi, Lei Feng, Feng Liu, Gang Niu, Masashi Sugiyama

llms computer-vision multimodal benchmarks

arXiv AI

Aug 5

In-Context Collapse in Vision-Language Models and How to Mitigate it?

arXiv:2608. 02830v1 Announce Type: cross Abstract: Many-shot in-context learning (ICL) lets vision-language models (VLMs) adapt from image--label demonstrations without weight updates, and is widely assumed to improve as more demonstrations are supplied.

By Mohammad Rostami

llms computer-vision multimodal benchmarks

Related stories