To automate the process of segmenting an anatomy of interest, we can learn a model from previously annotated data. The learning-based approach uses annotations to train a model that tries to emulate the expert labeling on a new data set. While tremendous progress has been made using such approaches, labeling of medical images remains a time-consuming and expensive task. In this paper, we evaluate the utility of extreme points in learning to segment. Specifically, we propose a novel approach to compute a confidence map from extreme points that quantitatively encodes the priors derived from extreme points. We use the confidence map as a cue to train a deep neural network based on ResNet-101 and PSP module to develop a class-agnostic segmentation model that outperforms state-of-the-art method that employs extreme points as a cue. Further, we evaluate a realistic use-case by using our model to generate training data for supervised learning (U-Net) and observed that U-Net performs comparably when trained with either the generated data or the ground truth data. These findings suggest that models trained using cues can be used to generate reliable training data.
Weakly and Self-Supervised Class-Agnostic Motion Prediction for Autonomous Driving
College of Computing and Data Science, Nanyang Technological University, Singapore（计算与数据科学学院，南洋理工大学，新加坡）；SenseTime Research, Hong Kong, China（时光科技研究院，香港，中国）
Beyond Textual CoT: Interleaved Text-Image Chains with Deep Confidence Reasoning for Image Editing
Shanghai Jiaotong University（上海交通大学）；Huawei Noah’s Ark Lab（华为诺亚实验室）；East China Normal University（华东师范大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）
Disentangling Hardness from Noise: An Uncertainty-Driven Model-Agnostic Framework for Long-Tailed Remote Sensing Classification
Research Center for Space Computing System, Zhejiang Lab（空间计算系统研究中心，浙江实验室）；School of Cyber Science and Engineering, Zhengzhou University（网络安全工程学院，郑州大学）；School of Computer Science and Technology, Xi'an Jiaotong University（计算机科学与技术学院，西安交通大学）
GVC-Seg: Training-Free 3D Instance Segmentation via Geometric Visual Correspondence
Victoria University of Wellington（惠灵顿维多利亚大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Southern University of Science and Technology（南方科技大学）
