Transfer learning aims at improving the performance of target learners on target domains by
transferring the knowledge contained in different but related source domains. In this way, the …

Semantic segmentation, also called scene labeling, refers to the process of assigning a
semantic label (eg car, people, and road) to each pixel of an image. It is an essential data …

Prevalent semantic segmentation solutions, despite their different network designs (FCN
based or attention based) and mask decoding strategies (parametric softmax based or pixel …

摘要近年来, 卷积神经网络(Convolutional neural networks, CNN) 已在图像理解领域得到了
广泛的应用, 引起了研究者的关注. 特别是随着大规模图像数据的产生以及计算机硬件(特别是 …

For the last few decades, several major subfields of artificial intelligence including computer
vision, graphics, and robotics have progressed largely independently from each other …

Large-scale training data with high-quality annotations is critical for training semantic and
instance segmentation models. Unfortunately, pixel-wise annotation is labor-intensive and …

We focus on the challenging task of real-time semantic segmentation in this paper. It finds
many practical applications and yet is with fundamental difficulty of reducing a large portion …

Scene parsing is challenging for unrestricted open vocabulary and diverse scenes. In this
paper, we exploit the capability of global context information by different-region-based …

Semantic classes can be either things (objects with a well-defined shape, eg car, person) or
stuff (amorphous background regions, eg grass, sky). While lots of classification and …

Semantic segmentation is a challenging task in computer vision systems. A lot of methods
have been developed to tackle this problem ranging from autonomous vehicles, human …