Unleashing the Potential of All Test Samples: Mean-Shift Guided Test-Time Adaptation

• URL: http://arxiv.org/abs/2507.00462v1
• Date: Tue, 01 Jul 2025 06:22:00 GMT
• Title: Unleashing the Potential of All Test Samples: Mean-Shift Guided Test-Time Adaptation
• Authors: Jizhou Han, Chenhao Ding, SongLin Dong, Yuhang He, Xinyuan Gao, Yihong Gong,
• Abstract summary: Existing training-free test-time adaptation methods operate strictly within CLIP's original feature space.<n>We propose MS-TTA, a training-free approach that enhances feature representations beyond CLIP's space using a single-step k-nearest neighbors (kNN) Mean-Shift.
• Score: 18.82879703518279
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual-language models (VLMs) like CLIP exhibit strong generalization but struggle with distribution shifts at test time. Existing training-free test-time adaptation (TTA) methods operate strictly within CLIP's original feature space, relying on high-confidence samples while overlooking the potential of low-confidence ones. We propose MS-TTA, a training-free approach that enhances feature representations beyond CLIP's space using a single-step k-nearest neighbors (kNN) Mean-Shift. By refining all test samples, MS-TTA improves feature compactness and class separability, leading to more stable adaptation. Additionally, a cache of refined embeddings further enhances inference by providing Mean Shift enhanced logits. Extensive evaluations on OOD and cross-dataset benchmarks demonstrate that MS-TTA consistently outperforms state-of-the-art training-free TTA methods, achieving robust adaptation without requiring additional training.

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