Edge detection is one of the most important and fundamental problems in the field of
computer vision and image processing. Edge contours extracted from images are widely …

We present a comprehensive study on a new task named camouflaged object detection
(COD), which aims to identify objects that are" seamlessly" embedded in their surroundings …

Weakly supervised semantic instance segmentation with only image-level supervision,
instead of relying on expensive pixel-wise masks or bounding box annotations, is an …

This work focuses on the extremely low-light image enhancement, which aims to improve
image brightness and reveal hidden information in darken areas. Recently, image …

The rapid spread of the new pandemic, ie, COVID-19, has severely threatened global
health. Deep-learning-based computer-aided screening, eg, COVID-19 infected CT area …

Recently, salient object detection (SOD) has witnessed vast progress with the rapid
development of convolutional neural networks (CNNs). However, the improvement of SOD …

Open-world instance segmentation is the task of grouping pixels into object instances
without any pre-determined taxonomy. This is challenging, as state-of-the-art methods rely …

We tackle the low-efficiency flaw of vision transformer caused by the high
computational/space complexity in Multi-Head Self-Attention (MHSA). To this end, we …

As a serious infectious disease, tuberculosis (TB) is one of the major threats to human health
worldwide, leading to millions of death every year. Although early diagnosis and treatment …

Real-time semantic segmentation of LiDAR data is crucial for autonomously driving vehicles
and robots, which are usually equipped with an embedded platform and have limited …