arXiv AI October 2, 2026 By Wei Luo, Yangfan Ou, Jin Deng, Zeshuai Deng, Xiquan Yan, Zhiquan Wen, Mingkui Tan

ProtoDCS: Towards Robust and Efficient Open-Set Test-Time Adaptation for Vision-Language Models

ProtoDCS introduces a robust open‑set test‑time adaptation framework for vision‑language models, addressing the challenge of simultaneously handling covariate‑shifted in‑distribution (csID) and out‑of‑distribution (csOOD) data. It replaces brittle thresholding with a double‑check separation using a probabilistic Gaussian Mixture Model and employs an evidence‑driven adaptation strategy that updates prototypes efficiently, reducing overconfidence and computational cost. Experiments on CIFAR‑10/100‑C and Tiny‑ImageNet‑C show state‑of‑the‑art performance, improving both known‑class accuracy and OOD detection metrics.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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 Machine Learning

Jul 1

T-QPM: Enabling Temporal Out-Of-Distribution Detection and Domain Generalization for Vision-Language Models in Open-World

arXiv:2603. 18481v2 Announce Type: replace-cross Abstract: Out-of-distribution (OOD) detection remains a critical challenge in open-world learning, where models must adapt to evolving data distributions.

By Aditi Naiknaware, Salimeh Sekeh

llms multimodal benchmarks

arXiv AI

Aug 20

From Inference to Adaptation: A Unified Optimal Transport View of Vision Language Model

arXiv:2608. 18339v1 Announce Type: cross Abstract: Vision-language models (VLMs) have demonstrated remarkable zero-shot capabilities yet remain sensitive to real-world distribution shifts during inference.

By Qi Yu, Zhichen Zeng, Katherine Tieu, Xiyuan Yang, Ruizhong Qiu, Yuchen Yan, Lihui Liu, Yanjun Zhao, Lingjie Chen, Jingrui He, Hanghang Tong

llms rag computer-vision multimodal benchmarks safety

arXiv Machine Learning

Jun 2

A Closer Look at In-Distribution vs. Out-of-Distribution Accuracy for Open-Set Test-time Adaptation

arXiv:2606. 01973v1 Announce Type: new Abstract: Open-set test-time adaptation (TTA) updates models on new data in the presence of input shifts and unknown output classes.

By Zefeng Li, Evan Shelhamer

benchmarks

arXiv Computer Vision

Sep 3

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption

The paper introduces UnInfo, a test‑time adaptation method for vision‑language models like CLIP that addresses image corruption—a realistic distribution shift caused by sensor conditions. UnInfo leverages uniformity‑aware confidence maximization, information‑aware loss balancing, and knowledge distillation from an EMA teacher to preserve embedding uniformity and improve zero‑shot classification accuracy. Experiments show that UnInfo outperforms existing TTA methods on corrupted image datasets.

By Kazuki Adachi, Shin'ya Yamaguchi, Tomoki Hamagami

llms rag fine-tuning efficiency multimodal

arXiv AI

Jul 13

TCLA: Training-Free Class-wise Logit Adaptation for Medical Vision-Language Models

arXiv:2607. 09562v1 Announce Type: cross Abstract: Medical Vision-Language Models (VLMs) exhibit strong zero-shot performance, yet their effectiveness still declines on out-of-distribution (OOD) data due to domain shifts and class bias inherited from large-scale pretraining.

By Tianyou Jiang, Ziyu Zhou

llms multimodal safety