Yixuan Ye — accepted AI-conference papers

2026

Bootstrap Dynamic-Aware 3D Visual Representation for Scalable Robot Learning

Qiwei Liang, Boyang Cai, Minghao Lai, Sitong Zhuang, Tao Lin, Yan Qin +3

CVPR 2026

Despite strong results on recognition and segmentation, current 3D visual pre-training methods often underperform on robotic manipulation. We attribute this gap to two factors: the lack of state-action-state dynamics modeling and the unnecessary redundancy of explicit geometric reconstruction. We in

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2026

Refinement Contrastive Learning of Cell–Gene Associations for Unsupervised Cell Type Identification

Liang Peng, Haopeng Liu, Yixuan Ye, Cheng Liu, Wenjun Shen, Si Wu +1

AAAI 2026technical

Unsupervised cell type identification is crucial for uncovering and characterizing heterogeneous populations in single cell omics studies. Although a range of clustering methods have been developed, most focus exclusively on intrinsic cellular structure and ignore the pivotal role of cell-gene assoc

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