676 citations · 4.7k across the 170 of their papers we have counts for
12 papers · 1 filter
Learning to Segment Object Candidates via Recursive Neural Networks
Tianshui Chen, Liang Lin, Xian Wu +2
To avoid the exhaustive search over locations and scales, current state-of-the-art object detection systems usually involve a crucial component generating a batch of candidate obje…
Human Pose Estimation from Depth Images via Inference Embedded Multi-task Learning
Keze Wang, Shengfu Zhai, Hui Cheng +2
Human pose estimation (i.e., locating the body parts / joints of a person) is a fundamental problem in human-computer interaction and multimedia applications. Significant progress…
Is Faster R-CNN Doing Well for Pedestrian Detection?
Liliang Zhang, Liang Lin, Xiaodan Liang +1
Detecting pedestrian has been arguably addressed as a special topic beyond general object detection. Although recent deep learning object detectors such as Fast/Faster R-CNN [1, 2]…
Local- and Holistic- Structure Preserving Image Super Resolution via Deep Joint Component Learning
Yukai Shi, Keze Wang, Li Xu +1
Recently, machine learning based single image super resolution (SR) approaches focus on jointly learning representations for high-resolution (HR) and low-resolution (LR) image patc…
DARI: Distance metric And Representation Integration for Person Verification
Guangrun Wang, Liang Lin, Shengyong Ding +2
The past decade has witnessed the rapid development of feature representation learning and distance metric learning, whereas the two steps are often discussed separately. To explor…
Geometric Scene Parsing with Hierarchical LSTM
Zhanglin Peng, Ruimao Zhang, Xiaodan Liang +2
This paper addresses the problem of geometric scene parsing, i.e. simultaneously labeling geometric surfaces (e.g. sky, ground and vertical plane) and determining the interaction r…
LSTM-CF: Unifying Context Modeling and Fusion with LSTMs for RGB-D Scene Labeling
Zhen Li, Yukang Gan, Xiaodan Liang +3
Semantic labeling of RGB-D scenes is crucial to many intelligent applications including perceptual robotics. It generates pixelwise and fine-grained label maps from simultaneously…
Deep Structured Scene Parsing by Learning with Image Descriptions
Liang Lin, Guangrun Wang, Rui Zhang +3
This paper addresses a fundamental problem of scene understanding: How to parse the scene image into a structured configuration (i.e., a semantic object hierarchy with object inter…
Joint Detection and Identification Feature Learning for Person Search
Tong Xiao, Shuang Li, Bochao Wang +2
Existing person re-identification benchmarks and methods mainly focus on matching cropped pedestrian images between queries and candidates. However, it is different from real-world…
Semantic Object Parsing with Graph LSTM
Xiaodan Liang, Xiaohui Shen, Jiashi Feng +2
By taking the semantic object parsing task as an exemplar application scenario, we propose the Graph Long Short-Term Memory (Graph LSTM) network, which is the generalization of LST…
Character Proposal Network for Robust Text Extraction
Shuye Zhang, Mude Lin, Tianshui Chen +2
Maximally stable extremal regions (MSER), which is a popular method to generate character proposals/candidates, has shown superior performance in scene text detection. However, the…
Learning Support Correlation Filters for Visual Tracking
Wangmeng Zuo, Xiaohe Wu, Liang Lin +2
Sampling and budgeting training examples are two essential factors in tracking algorithms based on support vector machines (SVMs) as a trade-off between accuracy and efficiency. Re…