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cs.CV2026
Revisiting Diffusion Fine-Tuning for Unsupervised Domain Adaptation
Xuan Qi, Yi Wei, Daniele Berardini +2
Diffusion-based unsupervised domain adaptation (UDA) improves cross-domain transfer by generating target-specific synthetic data for downstream adaptation. Existing methods are lar…
cs.CV2026
TReViS: Temporal Repetition Structure Aware Video Synthesis for Self-supervised Repetitive Action Counting
Fanqi Yu, Shengming Ma, Stefano Fiorini +4
Fully supervised repetitive action counting (RAC) has achieved strong performance, but requires dense temporal annotations that are costly and difficult to scale. We propose TReViS…
cs.CV2026
VT-DUDA: Visual Token Conditioning for Diffusion-guided Unsupervised Domain Adaptation
Xuan Qi, Daniele Berardini, Dario Serez +2
Unsupervised domain adaptation (UDA) aims to learn a target-domain classifier from labeled source data and unlabeled target data under distribution shift. Recent diffusion-based UD…