Introduces Dynamic Style Bridging for forward-facilitation continual test-time adaptation by multi-level style injection on pre-generated class proxies to provide stable on-demand supervision under evolving distribution shifts.
Beyond model adaptation at test time: A survey
6 Pith papers cite this work. Polarity classification is still indexing.
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An uncertainty-aware test-time adaptation framework improves cross-region spatio-temporal fusion of land surface temperature by updating only the fusion module guided by epistemic uncertainty, land use consistency, and bias correction.
Combines offline behavioral cloning with online Real-Time Recurrent RL fine-tuning on LrcSSM models to adapt autonomous driving policies to distribution shifts, validated in simulation and on a real 1:10-scale robot with event camera.
OD-TTA enables resource-efficient test-time adaptation on edge devices by triggering updates only on detected domain shifts, achieving comparable accuracy with lower energy and computation costs for embodied visual systems.
Ramen enables robust test-time adaptation of vision-language models under mixed-domain shifts by actively selecting domain-consistent and prediction-balanced samples via an embedding-gradient cache.
Edge AI systems require ongoing adaptation to evolving data and constraints to avoid violating budgets or losing reliability, formalized via an Agent-System-Environment lens that defines ten future research challenges.
citing papers explorer
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Dance Across Shifts: Forward-Facilitation Continual Test-Time Adaptation through Dynamic Style Bridging
Introduces Dynamic Style Bridging for forward-facilitation continual test-time adaptation by multi-level style injection on pre-generated class proxies to provide stable on-demand supervision under evolving distribution shifts.
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Uncertainty-Aware Test-Time Adaptation for Cross-Region Spatio-Temporal Fusion of Land Surface Temperature
An uncertainty-aware test-time adaptation framework improves cross-region spatio-temporal fusion of land surface temperature by updating only the fusion module guided by epistemic uncertainty, land use consistency, and bias correction.
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Adaptive Control in Autonomous Driving via Real-Time Recurrent RL
Combines offline behavioral cloning with online Real-Time Recurrent RL fine-tuning on LrcSSM models to adapt autonomous driving policies to distribution shifts, validated in simulation and on a real 1:10-scale robot with event camera.
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EmbodiTTA: Resource-Efficient Test-Time Adaptation for Embodied Visual Systems
OD-TTA enables resource-efficient test-time adaptation on edge devices by triggering updates only on detected domain shifts, achieving comparable accuracy with lower energy and computation costs for embodied visual systems.
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Ramen: Robust Test-Time Adaptation of Vision-Language Models with Active Sample Selection
Ramen enables robust test-time adaptation of vision-language models under mixed-domain shifts by actively selecting domain-consistent and prediction-balanced samples via an embedding-gradient cache.
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Position Paper: From Edge AI to Adaptive Edge AI
Edge AI systems require ongoing adaptation to evolving data and constraints to avoid violating budgets or losing reliability, formalized via an Agent-System-Environment lens that defines ten future research challenges.