Sparsely Annotated Object Detection (SAOD) tacklesthe issue of incomplete labeling in object detection. Comparedwith Fully Annotated Object Detection (FAOD), SAOD is more complicated and challenging. Unlabeled objects tend to provide wrong supervision to the detectors during training, resulting in ...
作者将数据集分为四种,分别是:全检测框 / 漏选一个检测框 / 漏选一般检测框 / 只保留一个检测框 消融实验 第一行为原图检测的分支,第二行为增强图检测的分支 论文信息 Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection ojs.aaai.org/index.php/ 发布于 2021-09-17 12:05 ...
2.1. Sparsely-supervised 3D object detection 近年来,由于标注边界框的成本较低,稀疏监督3D目标检测受到越来越多的关注。与全监督三维目标检测不同,稀疏监督方法仅需要少量的精确标注。因此,在稀疏监督设定下,需要生成伪标签来辅助检测器进行进一步训练。例如,SS3D【12】利用缺失标注实例挖掘(missing-annotated ins...
One of the core challenges in applying machine learning and artificial intelligence to medicine is the limited availability of annotated medical data. Unlike in other applications of machine learning, where an abundance of labeled data is available, the
Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection.Tiancai WangTong YangJiale CaoXiangyu ZhangNational Conference on Artificial Intelligence
A Novel Loss Calibration Strategy for Object Detection Networks Training on Sparsely Annotated Pathological DatasetsRecently, object detection frameworks based on Convolutional Neural Networks (CNNs) have become powerful methods for various tasks of medical image analysis; however, they often struggle with ...
image sampling,object detectionEfficient and reliable methods for training of object detectors are in higher demand than ever, and more and more data relevant to the field is becoming available. However, large datasets like Open Images Dataset v4 (OID) are sparsely annotated, and some measure ...
Shadow detectionSparse annotationReliable label propagationMulti-cue semantic calibrationSparsely annotated image segmentation has gained popularity due to its ability to significantly reduce the labeling burden on training data. However, existing methods still struggle to learn complete object structures, ...