Test-time Adaptation
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Benchmarks
ImageNet-C
Most implemented
Advances in Multimodal Adaptation and Generalization: From Traditional Approaches to Foundation Models
Continual Test-Time Adaptation in Computer Vision: Methods, Benchmarks, and Future Directions
Continual Test-Time Domain Adaptation
Distilling Image Prototypes for Guided Test-Time Adaptation
Papers
From Few-Shot Segmentation to Clinician-in-the-Loop Medical Image Analysis
Few-shot medical image segmentation (FSMIS) seeks to delineate unseen structures from a small support set, but its standard formulation fixes task-defining evidence before inference. This assumption is fragile when query…
Medical Image SegmentationInteractive SegmentationTest-time AdaptationDistilling Image Prototypes for Guided Test-Time Adaptation
Test-Time Adaptation (TTA) enhances the robustness of models against distribution shifts but faces two critical challenges: error accumulation from noisy pseudo-labels and catastrophic forgetting of source knowledge. Unc…
Test-time AdaptationAugmenting Human Performance with an XR Agent Learning from Online Behavior and BCI Evidence
We present OLIVE, a framework for adapting a foundation model to provide real-time assistance in temporally demanding, high-stakes, and dynamic tasks. We show that passive EEG, fused online with behavioral evidence, can …
Test-time AdaptationAdapting Without Gradients: Affine Statistics Transport and What Its Certificate Can Tell You
Test-time adaptation (TTA) typically assumes that model parameters can be updated at inference time. This assumption is restrictive for inference-only accelerators, frozen or third-party models, and memory-constrained de…
Test-time AdaptationSafin-1: Safety from Within through Memory-Native State Evolution
Long-horizon complex tasks require foundation models to accumulate information, maintain internal states, and adapt over extended interactions. Safety should be an intrinsic property of the model itself, rather than a be…
Long-Context UnderstandingTest-time AdaptationRECAST: Recent & Context-Aware Sampling for Test-Time Adaptation in Streaming Biosignals
Streaming biosignals vary across subjects and drift over time, so population-trained models lose accuracy during long-term monitoring. Test-time adaptation (TTA) enables online personalization by updating the model on in…
Test-time Adaptation