Recent years have witnessed enormous progress in AI-related fields such as computer
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …

As one of the fundamental problems in the field of video understanding, video object
segmentation aims at segmenting objects of interest throughout the given video sequence …

Recently, directly detecting 3D objects from 3D point clouds has received increasing
attention. To extract object representation from an irregular point cloud, existing methods …

Perception in autonomous vehicles is often carried out through a suite of different sensing
modalities. Given the massive amount of openly available labeled RGB data and the advent …

Semantic scene understanding is important for various applications. In particular, self-driving
cars need a fine-grained understanding of the surfaces and objects in their vicinity. Light …

Current state-of-the-art methods for image segmentation form a dense image representation
where the color, shape and texture information are all processed together inside a deep …

Point cloud analysis is very challenging, as the shape implied in irregular points is difficult to
capture. In this paper, we propose RS-CNN, namely, Relation-Shape Convolutional Neural …

Existing semantic segmentation approaches either aim to improve the object's inner
consistency by modeling the global context, or refine objects detail along their boundaries …

Spatial pyramid pooling module or encode-decoder structure are used in deep neural
networks for semantic segmentation task. The former networks are able to encode multi …

A Pyramid Attention Network (PAN) is proposed to exploit the impact of global contextual
information in semantic segmentation. Different from most existing works, we combine …