Object detection, as of one the most fundamental and challenging problems in computer
vision, has received great attention in recent years. Over the past two decades, we have …

In computer vision, significant advances have been made on object detection with the rapid
development of deep convolutional neural networks (CNN). This paper provides a …

Object detection, one of the most fundamental and challenging problems in computer vision,
seeks to locate object instances from a large number of predefined categories in natural …

We study the problem of efficient object detection of 3D LiDAR point clouds. To reduce the
memory and computational cost, existing point-based pipelines usually adopt task-agnostic …

With the rise of deep convolutional neural networks, object detection has achieved
prominent advances in past years. However, such prosperity could not camouflage the …

Although it is well believed for years that modeling relations between objects would help
object recognition, there has not been evidence that the idea is working in the deep learning …

We investigate the problem of producing structured graph representations of visual scenes.
Our work analyzes the role of motifs: regularly appearing substructures in scene graphs. We …

It is well known that contextual and multi-scale representations are important for accurate
visual recognition. In this paper we present the Inside-Outside Net (ION), an object detector …

Humans perceive the three-dimensional structure of the world with apparent ease. However,
despite all of the recent advances in computer vision research, the dream of having a …

While the use of bottom-up local operators in convolutional neural networks (CNNs)
matches well some of the statistics of natural images, it may also prevent such models from …