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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.
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...
arXiv:2505. 00986v3 Announce Type: replace Abstract: Continual Test-time adaptation (CTTA) continuously adapts the deployed model on every incoming batch of data.
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.
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.
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.