Search Results for author: Chang Huang

Found 24 papers, 11 papers with code

Diversity Transfer Network for Few-Shot Learning

1 code implementation31 Dec 2019 Mengting Chen, Yuxin Fang, Xinggang Wang, Heng Luo, Yifeng Geng, Xin-Yu Zhang, Chang Huang, Wenyu Liu, Bo wang

The learning problem of the sample generation (i. e., diversity transfer) is solved via minimizing an effective meta-classification loss in a single-stage network, instead of the generative loss in previous works.

Few-Shot Learning

RDSNet: A New Deep Architecture for Reciprocal Object Detection and Instance Segmentation

1 code implementation11 Dec 2019 Shaoru Wang, Yongchao Gong, Junliang Xing, Lichao Huang, Chang Huang, Weiming Hu

To reciprocate these two tasks, we design a two-stream structure to learn features on both the object level (i. e., bounding boxes) and the pixel level (i. e., instance masks) jointly.

Instance Segmentation Object Detection +2

High Performance Visual Object Tracking with Unified Convolutional Networks

no code implementations26 Aug 2019 Zheng Zhu, Wei Zou, Guan Huang, Dalong Du, Chang Huang

In this paper, we propose an end-to-end framework to learn the convolutional features and perform the tracking process simultaneously, namely, a unified convolutional tracker (UCT).

Visual Object Tracking

Object Detection in Video with Spatial-temporal Context Aggregation

no code implementations11 Jul 2019 Hao Luo, Lichao Huang, Han Shen, Yuan Li, Chang Huang, Xinggang Wang

Without any bells and whistles, our method obtains 80. 3\% mAP on the ImageNet VID dataset, which is superior over the previous state-of-the-arts.

Video Object Detection

Mask Scoring R-CNN

2 code implementations CVPR 2019 Zhaojin Huang, Lichao Huang, Yongchao Gong, Chang Huang, Xinggang Wang

In this paper, we study this problem and propose Mask Scoring R-CNN which contains a network block to learn the quality of the predicted instance masks.

General Classification Instance Segmentation +1

Mancs: A Multi-task Attentional Network with Curriculum Sampling for Person Re-identification

no code implementations ECCV 2018 Cheng Wang, Qian Zhang, Chang Huang, Wenyu Liu, Xinggang Wang

We propose a novel deep network called Mancs that solves the person re-identification problem from the following aspects: fully utilizing the attention mechanism for the person misalignment problem and properly sampling for the ranking loss to obtain more stable person representation.

Person Re-Identification

Tracklet Association Tracker: An End-to-End Learning-based Association Approach for Multi-Object Tracking

no code implementations5 Aug 2018 Han Shen, Lichao Huang, Chang Huang, Wei Xu

The separation of the task requires to define a hand-crafted training goal in affinity learning stage and a hand-crafted cost function of data association stage, which prevents the tracking goals from learning directly from the feature.

Multi-Object Tracking Multiple Object Tracking +1

Reinforced Evolutionary Neural Architecture Search

1 code implementation1 Aug 2018 Yukang Chen, Gaofeng Meng, Qian Zhang, Shiming Xiang, Chang Huang, Lisen Mu, Xinggang Wang

To address this issue, we propose the Reinforced Evolutionary Neural Architecture Search (RE- NAS), which is an evolutionary method with the reinforced mutation for NAS.

Neural Architecture Search Semantic Segmentation

Unsupervised Domain Adaptive Re-Identification: Theory and Practice

3 code implementations30 Jul 2018 Liangchen Song, Cheng Wang, Lefei Zhang, Bo Du, Qian Zhang, Chang Huang, Xinggang Wang

We study the problem of unsupervised domain adaptive re-identification (re-ID) which is an active topic in computer vision but lacks a theoretical foundation.

General Classification Unsupervised Domain Adaptation

Elements of Effective Deep Reinforcement Learning towards Tactical Driving Decision Making

no code implementations1 Feb 2018 Jingchu Liu, Pengfei Hou, Lisen Mu, Yinan Yu, Chang Huang

Tactical driving decision making is crucial for autonomous driving systems and has attracted considerable interest in recent years.

Autonomous Driving Decision Making

UCT: Learning Unified Convolutional Networks for Real-time Visual Tracking

no code implementations10 Nov 2017 Zheng Zhu, Guan Huang, Wei Zou, Dalong Du, Chang Huang

Convolutional neural networks (CNN) based tracking approaches have shown favorable performance in recent benchmarks.

Real-Time Visual Tracking

Text Flow: A Unified Text Detection System in Natural Scene Images

no code implementations ICCV 2015 Shangxuan Tian, Yifeng Pan, Chang Huang, Shijian Lu, Kai Yu, Chew Lim Tan

With character candidates detected by cascade boosting, the min-cost flow network model integrates the last three sequential steps into a single process which solves the error accumulation problem at both character level and text line level effectively.

Scene Text Scene Text Detection +1

CNN-RNN: A Unified Framework for Multi-label Image Classification

1 code implementation CVPR 2016 Jiang Wang, Yi Yang, Junhua Mao, Zhiheng Huang, Chang Huang, Wei Xu

While deep convolutional neural networks (CNNs) have shown a great success in single-label image classification, it is important to note that real world images generally contain multiple labels, which could correspond to different objects, scenes, actions and attributes in an image.

Classification General Classification +2

Look and Think Twice: Capturing Top-Down Visual Attention With Feedback Convolutional Neural Networks

no code implementations ICCV 2015 Chunshui Cao, Xian-Ming Liu, Yi Yang, Yinan Yu, Jiang Wang, Zilei Wang, Yongzhen Huang, Liang Wang, Chang Huang, Wei Xu, Deva Ramanan, Thomas S. Huang

While feedforward deep convolutional neural networks (CNNs) have been a great success in computer vision, it is important to remember that the human visual contex contains generally more feedback connections than foward connections.

Deep Multiple Instance Learning for Image Classification and Auto-Annotation

no code implementations CVPR 2015 Jiajun Wu, Yinan Yu, Chang Huang, Kai Yu

The recent development in learning deep representations has demonstrated its wide applications in traditional vision tasks like classification and detection.

Classification General Classification +2

Multi-Objective Convolutional Learning for Face Labeling

no code implementations CVPR 2015 Sifei Liu, Jimei Yang, Chang Huang, Ming-Hsuan Yang

This paper formulates face labeling as a conditional random field with unary and pairwise classifiers.

Learning From Massive Noisy Labeled Data for Image Classification

no code implementations CVPR 2015 Tong Xiao, Tian Xia, Yi Yang, Chang Huang, Xiaogang Wang

To demonstrate the effectiveness of our approach, we collect a large-scale real-world clothing classification dataset with both noisy and clean labels.

Classification General Classification +1

Large Scale Strongly Supervised Ensemble Metric Learning, with Applications to Face Verification and Retrieval

1 code implementation25 Dec 2012 Chang Huang, Shenghuo Zhu, Kai Yu

Learning Mahanalobis distance metrics in a high- dimensional feature space is very difficult especially when structural sparsity and low rank are enforced to improve com- putational efficiency in testing phase.

Face Verification Metric Learning

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