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
Interactive Memory Learning for Long-Term Conversations
Recent advancements in large language models have significantly enhanced the capabilities of agents in modeling long-term conversations. Despite these successes, existing approaches typically adopt a static heuristic par…
Reinforcement LearningTest-time AdaptationSparse MLLM Anchors, Dense Adaptation: Breaking the Self-Referential Loop in Wild Test-Time Adaptation
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 signals, including pred…
Test-time AdaptationIMVS: Interactive Medical Volume Segmentation with Test-Time Adaptation - A New Method for Annotating Radiology Datasets
Annotating large radiology datasets is bottlenecked by the manual effort of delineating structures slice-by-slice in 3D volumes. Interactive methods reduce this effort but stay interaction-inefficient: slice-wise methods…
Test-time AdaptationFrom 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 Adaptation