Tail Cues, Principal Corrections: Plug-and-Play Rectification for Open-Set Test-Time Adaptation introduces TALOR, a lightweight module that rectifies features for non-stationary target streams by decomposing them into high-gain principal and low-gain tail coordinates. TALOR applies a soft-routed affine correction in the principal subspace, subtracts drift via weighted ridge regression, and reconstructs the feature for the source head. On ImageNet-C with six csOOD datasets, TALOR surpasses the next-best UniEnt by 2.2% H-score, runs 2.5× faster, and uses only 12% of its GPU memory, while also boosting COME to 68.4% H-score with minimal memory overhead.
By Yingkai Yang, Chaoqi Chen, Hui Huang
arXiv:2603.14254v2 Announce Type: replace
Abstract: Test-time adaptation (TTA) aims to improve model robustness under distribution shifts by adapting to unlabeled test data, but most existing methods...
By Ronghao Zhang, Shuaicheng Niu, Qi Deng, Yanjie Dong, Jian Chen, Runhao Zeng
arXiv:2505. 00986v3 Announce Type: replace Abstract: Continual Test-time adaptation (CTTA) continuously adapts the deployed model on every incoming batch of data.
By Xiao Ma, Young D. Kwon, Dong Ma
The paper introduces MASA, a method for Wild Test-Time Adaptation that uses a frozen multimodal large language model to provide structured semantic anchors, thereby avoiding the self-referential loop common in existing WTTA techniques. MASA selects a small, diverse set of reliability-ranked anchors, encodes their descriptions, propagates them to nearby test samples, and stores this visual‑semantic information in an online prototype memory. The stored descriptors enable lightweight adaptation of normalization parameters, and MASA is evaluated on the WTTA ImageNet‑C benchmark with ResNet and ViT backbones under limited‑batch, mixed‑domain, and imbalanced‑label‑shift scenarios.
By Zhenbin Wang, Lei Zhang, Lituan Wang, Yan Wang, Zhao Zhang, Wei Huang
arXiv:2606. 14299v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) such as CLIP have become a standard backbone for open-vocabulary recognition, yet their zero-shot predictions remain vulnerable to distribution shifts encountered at deployment.
By Jiazhen Huang, Xiao Chen, Zhiming Liu, Yaru Sun, Jingyan Jiang, Zhi Wang
DESA‑TTA introduces a low‑overhead test‑time adaptation technique for vision‑language object detectors that dynamically adjusts the teacher’s exponential moving average coefficient based on pseudo‑label confidence and box density. It also employs source anchoring to gradually pull student parameters back toward their pretrained values, mitigating cumulative drift. Experiments on multiple distribution shifts and two VLOD architectures demonstrate consistent gains, with a 14.5‑point AP₅₀ improvement on VOC‑C and a 55% higher inference throughput compared to the prior state‑of‑the‑art TTA method for YOLO‑World.
By Atif Belal, Lilian Hollard, Marco Pedersoli, Eric Granger
arXiv:2603.28678v2 Announce Type: replace
Abstract: We introduce PACE, a backpropagation-free continual test-time adaptation system that directly optimizes the affine parameters of normalization laye...
By Damian S\'ojka, Sebastian Cygert, Marc Masana
Long-video MLLMs must model temporal change before a limited visual-token budget removes most frame evidence. We introduce LongVU-TTT, which inserts a convolutional Test-Time Training (TTT) resampler...
arXiv:2609.36655v1 Announce Type: new
Abstract: Continual test-time adaptation (CTTA) adapts a source model to an unlabeled test stream whose distribution may change over time. Existing TTA methods o...
By Youjia Zhang, Huiling Liu, Soyun Choi, Jaehong Yoon, Sungeun Hong
The paper introduces CASTER, a gradient‑free test‑time adaptation method that keeps the model frozen by storing source class statistics in a discriminative subspace and applying an affine transformation estimated from target‑batch moments. CASTER avoids backward passes, optimizer state, and large feature banks, outperforming k‑NN on frozen features in most backbone‑dataset settings while using far less memory. The authors also propose a residual‑to‑margin transportability certificate that flags when affine transport is unreliable, and demonstrate that gating based on this certificate can recover performance losses.
By Salim Khazem, Ibrahim Mohamed Serouis
arXiv:2610.19700v1 Announce Type: new
Abstract: Low-rank adaptation (LoRA) enables parameter-efficient fine-tuning by learning low-rank weight updates. Increasing rank expands adaptation capacity, bu...
By Fanyao Li, Yuxi Liu, Xiaohao Liu, He Chang, Zhaoxin Wang, Zhulin Tao
Wild test-time adaptation (WTTA) updates a source model online under small test batches, concurrent distribution shifts, and time-varying class imbalance. Most WTTA methods derive their adaptation sig...