Author

Hyeong-Tae Cha

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#artificial intelligence Preprint Oct 2026

Later Is Better: Token Reduction for ViTs Under Distribution Shift

Training-free token reduction accelerates vision transformers by removing redundant tokens across layers, recovering most of the original accuracy at a fraction of the compute. These methods, however, are designed and evaluated primarily on clean data, and under real-world distribution shift their accuracy gap to the u...

Hyeong-Tae Cha, Hyungjun Yoon, Sung-Ju Lee · 0 citations

#computer vision Preprint Oct 2026

Test-Time Adaptation of Quantized ViTs via Single-Pass Quantizer-Aligned Recalibration

Quantizer-Aligned Recalibration (QuAR), a single-pass TTA method tailored to quantized ViTs that neither backpropagates nor updates any model parameters is proposed, which achieves the highest mean accuracy among state-of-the-art backpropagates nor updates any model parameters.

Hyeong-Tae Cha, Young D. Kwon, Sung-Ju Lee · 0 citations