In this paper, a new approach based on multiple instance learning is proposed to predict student’s performance and to improve the obtained results using a classical single instance learning. Multiple instance learning provides a more suitable and optimized representation that is adapted to available information of each student and course eliminating the missing values that make difficult to fin...

Abstract In recent years, instance segmentation has become a key research area in computer vision. This technology been applied varied applications such as robotics, healthcare and intelligent driving. Instance not only detects the location of object but also marks edges for each single instance, which can solve both detection semantic concurrently. Our survey will give detail introduction to b...

Long-tailed class distributions are prevalent among the practical applications of object detection and instance segmentation. Prior work in long-tail segmentation addresses imbalance losses between rare frequent categories by reducing penalty for a model incorrectly predicting label. We demonstrate that heavily suppressed correct background predictions, which reduces probability all foreground ...

Label propagation is a popular semi-supervised learning technique that transfers information from labeled examples to unlabeled examples through a graph. Most label propagation methods construct a graph based on example-to-example similarity, assuming that the resulting graph connects examples that share similar labels. Unfortunately, examplelevel similarity is sometimes badly defined. For inst...

Multi-Instance Learning (MIL) deals with problems where each training example is a bag, and each bag contains a set of instances. Multi-instance representation is useful in many real world applications, because it is able to capture more structural information than traditional flat single-instance representation. However, it also brings new challenges. Specifically, the distance between data ob...