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• Subtractive TTA methods excel under severe domain shifts by removing corrupted features, while additive methods are effective under moderate shifts requiring refinement.technical 90%
• CD-Buffer adaptively balances subtractive and additive TTA strategies based on measured feature-level domain shift.method 95%
• CD-Buffer achieves state-of-the-art performance on KITTI, Cityscapes, and ACDC datasets across diverse weather conditions and severity levels.result 90%
• The proposed method (CD-Buffer) achieves the best or comparably high performance in most cases under various adverse weather conditions, while other methods show inconsistent trends depending on severity.result 90%
• Additive TTA methods like BufferTTA and WHW perform relatively well under moderate shifts but suffer substantial degradation under severe conditions.result 85%
• The subtractive buffer module eliminates features that exhibit severe domain shift.method 90%
• CD-Buffer automatically balances channel-wise suppression and compensation without manual tuning.technical 95%

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