4 papers
Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification
Yinghao Jin, Xi Yang
Active learning (AL) aims to build high-quality labeled datasets by iteratively selecting the most informative samples from an unlabeled pool under limited annotation budgets. Howe…
Clustering Properties of Self-Supervised Learning
Xi Weng, Jianing An, Xudong Ma +5
Self-supervised learning (SSL) methods via joint embedding architectures have proven remarkably effective at capturing semantically rich representations with strong clustering prop…
OSIS: Efficient One-stage Network for 3D Instance Segmentation
Chuan Tang, Xi Yang
Current 3D instance segmentation models generally use multi-stage methods to extract instance objects, including clustering, feature extraction, and post-processing processes. Howe…
Parts2Words: Learning Joint Embedding of Point Clouds and Texts by Bidirectional Matching between Parts and Words
Chuan Tang, Xi Yang, Bojian Wu +2
Shape-Text matching is an important task of high-level shape understanding. Current methods mainly represent a 3D shape as multiple 2D rendered views, which obviously can not be un…