Image segmentation is a key task in computer vision and image processing with important
applications such as scene understanding, medical image analysis, robotic perception …

This paper presents a comprehensive survey on vision-based robotic grasping. We
conclude three key tasks during vision-based robotic grasping, which are object localization …

Although convolutional neural networks (CNNs) have achieved great success in computer
vision, this work investigates a simpler, convolution-free backbone network useful for many …

Boundary discontinuity and its inconsistency to the final detection metric have been the
bottleneck for rotating detection regression loss design. In this paper, we propose a novel …

Deep learning-based object detection and instance segmentation have achieved
unprecedented progress. In this article, we propose complete-IoU (CIoU) loss and Cluster …

We present a high-performance method that can achieve mask-level instance segmentation
with only bounding-box annotations for training. While this setting has been studied in the …

Segmenting highly-overlapping objects is challenging, because typically no distinction is
made between real object contours and occlusion boundaries. Unlike previous two-stage …

Most recent transformer-based models show impressive performance on vision tasks, even
better than Convolution Neural Networks (CNN). In this work, we present a novel, flexible …

Abstract Few-shot learning (FSL), which aims to recognise new classes by adapting the
learned knowledge with extremely limited few-shot (support) examples, remains an …

In this paper, we propose an end-to-end framework for instance segmentation. Based on the
recently introduced DETR, our method, termed SOLQ, segments objects by learning unified …