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

Full-image dependencies provide useful contextual information to benefit visual
understanding problems. In this work, we propose a Criss-Cross Network (CCNet) for …

We propose ResRep, a novel method for lossless channel pruning (aka filter pruning), which
slims down a CNN by reducing the width (number of output channels) of convolutional …

We consider the problem of unsupervised domain adaptation for semantic segmentation by
easing the domain shift between the source domain (synthetic data) and the target domain …

Self-supervised monocular depth estimation has been widely studied, owing to its practical
importance and recent promising improvements. However, most works suffer from limited …

Incorporating the depth (D) information to RGB images has proven the effectiveness and
robustness in semantic segmentation. However, the fusion between them is not trivial due to …

The success of deep learning in visual recognition tasks has driven advancements in
multiple fields of research. Particularly, increasing attention has been drawn towards its …

Compared with traditional handcrafted features, deep learning has greatly improved the
performance of scene parsing. However, it remains challenging under various …

In this paper, we study the problem of salient object detection (SOD) for RGB-D images
using both color and depth information. A major technical challenge in performing salient …

We recently demonstrated the remarkable performance of scene parsing, and one of its
aspects was shown to be relevant to performance, namely, generation of multilevel feature …