Image segmentation, which has become a research hotspot in the field of image processing
and computer vision, refers to the process of dividing an image into meaningful and non …

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

Recurrent neural networks (RNNs) have been widely adopted in research areas concerned
with sequential data, such as text, audio, and video. However, RNNs consisting of sigma …

Lots of learning tasks require dealing with graph data which contains rich relation
information among elements. Modeling physics systems, learning molecular fingerprints …

In this paper, we address the scene segmentation task by capturing rich contextual
dependencies based on the self-attention mechanism. Unlike previous works that capture …

Deep learning on graphs has become one of the hottest topics in machine learning. The
book consists of four parts to best accommodate our readers with diverse backgrounds and …

Document-level relation extraction (RE) poses new challenges compared to its sentence-
level counterpart. One document commonly contains multiple entity pairs, and one entity pair …

In crowd scenarios, reliable trajectory prediction of pedestrians requires insightful
understanding of their social behaviors. These behaviors have been well investigated by …

Lane detection is one of the most important tasks in self-driving. Due to various complex
scenarios (eg, severe occlusion, ambiguous lanes, etc.) and the sparse supervisory signals …

We propose a novel deep learning-based framework to tackle the challenge of semantic
segmentation of large-scale point clouds of millions of points. We argue that the organization …