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洞見 - Continual Test-Time Adaptation for Semantic Segmentation
Hybrid-TTA: Dynamically Adapting Segmentation Models to Continually Changing Target Environments
Hybrid-TTA dynamically selects between Full-Tuning and Efficient-Tuning strategies to effectively adapt segmentation models to continually changing target environments, leveraging a Masked Image Modeling based Adaptation framework for robust and efficient continual adaptation.
Efficient and Stable Continual Test-Time Adaptation for Semantic Segmentation
A parameter-efficient model-based method named distribution-aware tuning (DAT) that adaptively selects and updates two small groups of trainable parameters to extract target domain-specific and task-relevant knowledge, effectively addressing issues of error accumulation and catastrophic forgetting during continual adaptation.