Traditional CNN-based training for instance segmentation is time-consuming owing to large
datasets and complex network modules, making direct searching of architecture …

Instance segmentation is a challenging task in computer vision, as it requires distinguishing
objects and predicting dense areas. Currently, segmentation models based on complex …

In this paper, we propose a conceptually novel, efficient, and fully convolutional framework
for real-time instance segmentation. Previously, most instance segmentation methods …

Although instance segmentation has made significant progress in recent years, it is still a
challenge to develop highly accurate algorithms with real-time performance. In this paper …

Instance segmentation needs to locate all instances in an image correctly and segment each
instance precisely. Currently, the most dominant methods for instance segmentation take …

There are often dense objects in the images processed by instance segmentation, but too
dense objects will cause the problem that the objects are difficult to segment. Most of the …

Real-time instance segmentation is crucial in various AI applications. This work designs a
network named Fast Attention based Single-Stage Segmentation NeT (FASSST) that …

SparseInst, a recent lightweight instance segmentation network, achieves better balance
between efficiency and precision. However, the information contained in the single-layer …

Instance segmentation is one of the general but challenging tasks in computer vision. In this
paper, we present a novel fast single-stage instance segmentation method, called CIMask …

This paper proposes a novel instance segmentation model, which improves the instance
segmentation by considering two aspects. One is a new non-local features module to …