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.
Test-time training with self- supervision for generalization under distribution shifts
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CV 4years
2026 4representative citing papers
TokenGS uses learnable Gaussian tokens in an encoder-decoder architecture to regress 3D means directly, achieving SOTA feed-forward reconstruction on static and dynamic scenes with better robustness.
SeeCo is a training-free on-the-fly recalibration method using multi-view geometric consistency and adaptive textual calibration to improve open-vocabulary semantic segmentation in remote sensing images.
SATTC improves top-k accuracy in cross-subject EEG-to-image retrieval by fusing geometric whitening and structural nearest-neighbor experts on the similarity matrix without labels.
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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TokenGS: Decoupling 3D Gaussian Prediction from Pixels with Learnable Tokens
TokenGS uses learnable Gaussian tokens in an encoder-decoder architecture to regress 3D means directly, achieving SOTA feed-forward reconstruction on static and dynamic scenes with better robustness.
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Seeking Consensus: Geometric-Semantic On-the-Fly Recalibration for Open-Vocabulary Remote Sensing Semantic Segmentation
SeeCo is a training-free on-the-fly recalibration method using multi-view geometric consistency and adaptive textual calibration to improve open-vocabulary semantic segmentation in remote sensing images.
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SATTC: Structure-Aware Label-Free Test-Time Calibration for Cross-Subject EEG-to-Image Retrieval
SATTC improves top-k accuracy in cross-subject EEG-to-image retrieval by fusing geometric whitening and structural nearest-neighbor experts on the similarity matrix without labels.