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cs.CV2026

ToPO: Token-Oriented Preference Optimization for Text-to-Image Diffusion Models

Juntao Xu, Shihong Li, Hoi Fan Au +1

Diffusion models have achieved remarkable success in text-to-image generation, yet aligning their outputs with human preferences remains challenging. Diffusion-DPO learns from imag…

cs.CV2026

SUGFW+: An Uncertainty-guided Feature Weighting Framework for Cold Start Active Adaptation of SAM in Medical Image Segmentation

Xiaochuan Ma, Ning Zhu, Jia Fu +5

Cold Start Active Learning (CSAL) is important in improving the performance of a medical image segmentation model with low annotation budget by querying a small subset for annotati…

cs.CV2026

Rectify Then Diffuse: Disentangling Concepts Before Denoising Trajectory Unfolds

Ning Zhu, An Chen, Mengfei Zhao +4

Text-to-image diffusion models can generate individual concepts well, but they often omit or merge concepts incorrectly with multiple concepts. We trace these failures to an early…

cs.CV2026

Stabilizing Temporal Inference Dynamics for Online Surgical Phase Recognition

Yang Liu, Ning Zhu, Jingjing Peng +4

Online Surgical Phase Recognition (SPR) models can reach high frame-wise accuracy, yet their predictions often lack temporal stability, fragmenting workflow understanding and reduc…

cs.CV2025

MedCAL-Bench: A Comprehensive Benchmark on Cold-Start Active Learning with Foundation Models for Medical Image Analysis

Ning Zhu, Xiaochuan Ma, Shaoting Zhang +1

Cold-Start Active Learning (CSAL) aims to select informative samples for annotation without prior knowledge, which is important for improving annotation efficiency and model perfor…