• Publications
  • Influence
Supervised Discrete Hashing
Recently, learning based hashing techniques have attracted broad research interests because they can support efficient storage and retrieval for high-dimensional data such as images, videos,Expand
  • 718
  • 191
  • Open Access
RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation
Recently, very deep convolutional neural networks (CNNs) have shown outstanding performance in object recognition and have also been the first choice for dense classification problems such asExpand
  • 1,036
  • 159
  • Open Access
Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections
In this paper, we propose a very deep fully convolutional encoding-decoding framework for image restoration such as denoising and super-resolution. The network is composed of multiple layers ofExpand
  • 664
  • 83
  • Open Access
FCOS: Fully Convolutional One-Stage Object Detection
We propose a fully convolutional one-stage object detector (FCOS) to solve object detection in a per-pixel prediction fashion, analogue to semantic segmentation. Almost all state-of-the-art objectExpand
  • 286
  • 79
  • Open Access
Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields
In this article, we tackle the problem of depth estimation from single monocular images. Compared with depth estimation using multiple images such as stereo depth perception, depth from monocularExpand
  • 607
  • 61
  • Open Access
Fast Supervised Hashing with Decision Trees for High-Dimensional Data
Supervised hashing aims to map the original features to compact binary codes that are able to preserve label based similarity in the Hamming space. Non-linear hash functions have demonstrated theirExpand
  • 346
  • 61
  • Open Access
Semidefinite Programming
  • 668
  • 50
Deep convolutional neural fields for depth estimation from a single image
We consider the problem of depth estimation from a single monocular image in this work. It is a challenging task as no reliable depth cues are available, e.g., stereo correspondences, motions etc.Expand
  • 547
  • 47
  • Open Access
VITAL: VIsual Tracking via Adversarial Learning
The tracking-by-detection framework consists of two stages, i.e., drawing samples around the target object in the first stage and classifying each sample as the target object or as background in theExpand
  • 183
  • 41
  • Open Access
Repulsion Loss: Detecting Pedestrians in a Crowd
Detecting individual pedestrians in a crowd remains a challenging problem since the pedestrians often gather together and occlude each other in real-world scenarios. In this paper, we first exploreExpand
  • 149
  • 39
  • Open Access