Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

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

Understanding the world in 3D is a critical component of urban autonomous driving.
Generally, the combination of expensive LiDAR sensors and stereo RGB imaging has been …

To reduce the significant redundancy in deep Convolutional Neural Networks (CNNs), most
existing methods prune neurons by only considering the statistics of an individual layer or …

Image manipulation detection is different from traditional semantic object detection because
it pays more attention to tampering artifacts than to image content, which suggests that richer …

We consider open-domain queston answering (QA) where answers are drawn from either a
corpus, a knowledge base (KB), or a combination of both of these. We focus on a setting in …

Detecting objects in aerial images is challenging for at least two reasons:(1) target objects
like pedestrians are very small in pixels, making them hardly distinguished from surrounding …

Action recognition is an open and challenging problem in computer vision. While current
state-of-the-art models offer excellent recognition results, their computational expense limits …

Deep neural networks have become a mainstream approach to interactive segmentation. As
we show in our experiments, while for some images a trained network provides accurate …