7 papers
TUCO: Curating Simulation Demonstrations for Sim-to-Real Robot Policy Co-Training
Ning Zhu, Mengfei Zhao, Yikai Tang +5
Simulation demonstrations can supplement scarce real-world data for robot policy co-training. However, the value of using data curation to actively select these demonstrations for…
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…
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…
One Knob to Rule Them All: A Unified Optimal Transport View of Cold-Start Active Learning
Ning Zhu, Xiaochuan Ma, Juntao Xu +4
Cold-Start Active Learning (CSAL) aims to select a valuable subset from an unlabeled pool without any prior knowledge or human assistance. Existing methods take diverse routes base…
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…
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…
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…