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, 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 …

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

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 …

We study the recent progress on dynamic view synthesis (DVS) from monocular video.
Though existing approaches have demonstrated impressive results, we show a discrepancy …

In this paper, we address the semantic segmentation task with a new context aggregation
scheme named object context, which focuses on enhancing the role of object information …

With the rapid development of deep learning techniques, deep convolutional neural
networks (DCNNs) have become more important for object detection. Compared with …

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 …

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 …

In this paper, we address the semantic segmentation task with a new context aggregation
scheme named\emph {object context}, which focuses on enhancing the role of object …