Existing instance segmentation models mostly pay less attention to the targeted characteristics of ships in synthetic aperture radar (SAR) images, which hinders further accuracy improvements, leading poor performance more complex SAR image scenes. To solve this problem, we propose a hybrid task cascade plus (HTC+) for better ship segmentation. Aiming at specific task, seven techniques are propo...

With growing awareness of societal impact artificial intelligence, fairness has become an important aspect machine learning algorithms. The issue is that human biases towards certain groups population, defined by sensitive features like race and gender, are introduced to the training data through collection labeling. Two directions ensuring research have focused on (i) instance weighting in ord...

Abstract Can our video understanding systems perceive objects when a heavy occlusion exists in scene? To answer this question, we collect large-scale dataset called OVIS for occluded instance segmentation, that is, to simultaneously detect, segment, and track instances scenes. consists of 296k high-quality masks from 25 semantic categories, where object occlusions usually occur. While human vis...

Multiple instance learning is qualified for many pattern recognition tasks with weakly annotated data. The combination of artificial neural network and multiple offers an end-to-end solution has been widely utilized. However, challenges remain in two-folds. Firstly, current MIL pooling operators are usually pre-defined lack flexibility to mine key instances. Secondly, solutions, the bag-level r...

Circle representation has recently been introduced as a “medical imaging optimized" for more effective instance object detection on ball-shaped medical objects. With its superior performance detection, it is appealing to extend the circle segmentation. In this work, we propose CircleSnake, simple end-to-end contour deformation-based segmentation method Compared prevalent DeepSnake method, our c...

In multiple-Instance Learning (MIL), training class labels are attached to sets of bags composed of unlabeled instances, and the goal is to deal with classification of bags. Most previous MIL algorithms, which tackle classification problems, consider each instance as a represented feature. Although the algorithms work well in some prediction problems, considering diverse features to represent a...