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
Learning to Predict Distributions over Weight Updates for Test-Time Adaptation
Hypernetworks have recently shown success in dynamically adapting the parameters of Large Language Models (LLMs) at runtime based on signals such as task descriptions or additional demostrations. Here we ask: how much ad…
Test-time AdaptationWISE-ATTA: When to Ask for Labels in Budgeted Active Test-Time Adaptation
Active test-time adaptation (ATTA) improves robustness under distribution shift by updating a deployed model during inference while selectively querying supervision. However, most existing ATTA methods implicitly assume …
Test-time AdaptationLabel-free steering: Compressing test-time reinforcement learning into bias-only subspaces
Test-time reinforcement learning (TTRL) enables models to improve their reasoning without relying on labeled training data, but existing approaches typically optimize a large fraction of the model parameters. This raises…
Reinforcement LearningTest-time AdaptationInteractive 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 Adaptation