arXiv AI June 9, 2026 By Xiaoran Xu, Yifan Xu, Yupeng Wu, Xiaoshan Yang, Changsheng Xu

DOME: Learning Transferable Domain Variables from Sparse Supervision for Test-Time Adaptation

arXiv:2606. 07646v1 Announce Type: cross Abstract: Test-time adaptation (TTA) aims to align a model to shifting test domains using only unlabeled streaming data.

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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

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arXiv AI

Jul 3

AnyGroundBench: A Specialized-Domain Benchmark for Video Grounding in Vision-Language Models

arXiv:2607. 02269v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have demonstrated immense promise in Spatio-Temporal Video Grounding (STVG).

By Rintaro Otsubo, Ryo Fujii, Reina Ishikawa, Taiki Kanaya, Kanta Sawafuji, Hiroki Kajita, Shigeki Sakai, Hideo Saito, Ryo Hachiuma

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arXiv Machine Learning

Sep 22

SPeaR: Test-Time Adaptation with Steering Primitives for Realigning Representations

arXiv:2609.24111v1 Announce Type: new Abstract: Test-time adaptation (TTA) addresses distribution shift using only unlabeled test data. Existing methods typically adapt pretrained models by updating...

By Muhammad Sudipto Siam Dip, Ali Etemad

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arXiv AI

Aug 5

Efficient unsupervised domain adaptation via self-supervised vision transformer and synergistic cross-domain alignment

arXiv:2407. 21311v2 Announce Type: replace-cross Abstract: Unsupervised domain adaptation (UDA) aims to mitigate domain shift, where the distribution of labeled source data differs from that of unlabeled target data.

By Ali Abedi, Q. M. Jonathan Wu, Ning Zhang, Farhad Pourpanah

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arXiv Machine Learning

Jul 21

DA-MergeLoRA: Hypernetwork-Based LoRA Merging for Few-Shot Test-Time Domain Adaptation

arXiv:2607. 17467v1 Announce Type: cross Abstract: Few-shot Test-Time Domain Adaptation (FSTT-DA) seeks to adapt models to novel domains using only a handful of unlabeled target samples.

By Siobhan Reid, Zhixiang Chi, Li Gu, Omid Reza Heidari, Ziqiang Wang, Yang Wang

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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

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

AnyGroundBench: A Specialized-Domain Benchmark for Video Grounding in Vision-Language Models

SPeaR: Test-Time Adaptation with Steering Primitives for Realigning Representations

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