Significant progress has been made in boundary detection with the help of convolutional neural networks. Recent boundary detection models not only focus on real object boundary detection but also "crisp" boundaries (precisely localized along the object's contour). There are two methods to evaluate crisp boundary performance. One uses more strict tolerance to measure the distance between the ground truth and the detected contour. The other focuses on evaluating the contour map without any postprocessing. In this study, we analyze both methods and conclude that both methods are two aspects of crisp contour evaluation. Accordingly, we propose a novel network named deep refinement network (DRNet) that stacks multiple refinement modules to achieve richer feature representation and a novel loss function, which combines cross-entropy and dice loss through effective adaptive fusion. Experimental results demonstrated that we achieve state-of-the-art performance for several available datasets.
Learning Crisp Edge Detector Using Logical Refinement Network
Tencent Jarvis Lab（腾讯贾维斯实验室）
边缘检测是各种计算机视觉任务中的一个基本问题。近年来，基于深度学习的边缘检测算法取得了令人满意的改进。虽然他们中的大多数报告良好的评价分数，他们往往无法准确地定位边缘，并给出厚和模糊的边界。此外，他们中的大多数集中在2D图像和具有挑战性的3D边缘检测仍然是探索不足。在这项工作中，我们提出了一种新的逻辑细化网络清晰的边缘检测，这是出于分割和边缘图之间的逻辑关系，可以应用于2D和3D图像。该网络由一个联合的目标和边缘检测网络和一个清晰的边缘细化网络组成，它预测更准确，更清晰，更薄的高质量的二值边缘图，而无需任何后处理。对Kaggle 2018 Data Science Bowl的2D细胞核图像和猴脑的私人3D显微镜图像进行了广泛的实验，与最先进的方法相比，这些图像显示出出色的性能。
Deep Crisp Boundaries: From Boundaries to Higher-level Tasks
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；CAS Center for Excellence in Brain Science and Intelligence Technology（中国科学院脑科学与智能技术卓越创新中心）
Learning to utilize image second-order derivative information for crisp edge detection
School of Microelectronics, Tianjin University（天津大学微电子学院）；College of Electronic Information and Optical Engineering, Nankai University（南开大学电子信息与光学工程学院）；College of Information Engineering, Luoyang Polytechnic（洛阳职业技术学院信息工程学院）；Tianjin Fire Science and Technology Research Institute of MEM（天津消防科学技术研究所）；School of Transportation Science and Engineering, Beihang University（北京航空航天大学交通科学与工程学院）
CIR-Net: Cross-modality Interaction and Refinement for RGB-D Salient Object Detection
Beijing Jiaotong University（北京交通大学）；Nanyang Technological University（南洋理工大学）；Sun Yat-sen University（中山大学）；University of Chinese Academy of Sciences（中国科学院大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Peng Cheng Laboratory（鹏城实验室）
From Calibration to Refinement: Seeking Certainty via Probabilistic Evidence Propagation for Noisy-Label Person Re-Identification
School of Computer Science and Technology, Wuhan University of Science and Technology（武汉科技大学计算机科学与技术学院）；Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan University of Science and Technology（湖北省智能信息处理与实时工业系统重点实验室）；School of Computer Science, Wuhan University（武汉大学计算机学院）；Department of Electrical Engineering and the Institute of Communications Engineering, National Tsing Hua University（清华大学电子工程系和通信工程研究所）；Electronic and Optoelectronic System Research Laboratories, Industrial Technology Research Institute（工业技术研究院电子与光电子系统研究实验室）
calibfusion: Transformer-Based Differentiable Calibration for Radar-Camera Fusion Detection in Water-Surface Environments
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
