142 citations · 354 across the 14 of their papers we have counts for
14 papers
Feature Completion for Occluded Person Re-Identification
Ruibing Hou, Bingpeng Ma, Hong Chang +3
Person re-identification (reID) plays an important role in computer vision. However, existing methods suffer from performance degradation in occluded scenes. In this work, we propo…
BiCnet-TKS: Learning Efficient Spatial-Temporal Representation for Video Person Re-Identification
Ruibing Hou, Hong Chang, Bingpeng Ma +2
In this paper, we present an efficient spatial-temporal representation for video person re-identification (reID). Firstly, we propose a Bilateral Complementary Network (BiCnet) for…
Continuity-Discrimination Convolutional Neural Network for Visual Object Tracking
Shen Li, Bingpeng Ma, Hong Chang +2
This paper proposes a novel model, named Continuity-Discrimination Convolutional Neural Network (CD-CNN), for visual object tracking. Existing state-of-the-art tracking methods do…
IAUnet: Global Context-Aware Feature Learning for Person Re-Identification
Ruibing Hou, Bingpeng Ma, Hong Chang +3
Person re-identification (reID) by CNNs based networks has achieved favorable performance in recent years. However, most of existing CNNs based methods do not take full advantage o…
Appearance-Preserving 3D Convolution for Video-based Person Re-identification
Xinqian Gu, Hong Chang, Bingpeng Ma +2
Due to the imperfect person detection results and posture changes, temporal appearance misalignment is unavoidable in video-based person re-identification (ReID). In this case, 3D…
Temporal Complementary Learning for Video Person Re-Identification
Ruibing Hou, Hong Chang, Bingpeng Ma +2
This paper proposes a Temporal Complementary Learning Network that extracts complementary features of consecutive video frames for video person re-identification. Firstly, we intro…
Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training
Hongkai Zhang, Hong Chang, Bingpeng Ma +2
Although two-stage object detectors have continuously advanced the state-of-the-art performance in recent years, the training process itself is far from crystal. In this work, we f…
Cross Attention Network for Few-shot Classification
Ruibing Hou, Hong Chang, Bingpeng Ma +2
Few-shot classification aims to recognize unlabeled samples from unseen classes given only few labeled samples. The unseen classes and low-data problem make few-shot classification…
Temporal Knowledge Propagation for Image-to-Video Person Re-identification
Xinqian Gu, Bingpeng Ma, Hong Chang +2
In many scenarios of Person Re-identification (Re-ID), the gallery set consists of lots of surveillance videos and the query is just an image, thus Re-ID has to be conducted betwee…
Interaction-and-Aggregation Network for Person Re-identification
Ruibing Hou, Bingpeng Ma, Hong Chang +3
Person re-identification (reID) benefits greatly from deep convolutional neural networks (CNNs) which learn robust feature embeddings. However, CNNs are inherently limited in model…
VRSTC: Occlusion-Free Video Person Re-Identification
Ruibing Hou, Bingpeng Ma, Hong Chang +3
Video person re-identification (re-ID) plays an important role in surveillance video analysis. However, the performance of video re-ID degenerates severely under partial occlusion.…
Cascade RetinaNet: Maintaining Consistency for Single-Stage Object Detection
Hongkai Zhang, Hong Chang, Bingpeng Ma +2
Recent researches attempt to improve the detection performance by adopting the idea of cascade for single-stage detectors. In this paper, we analyze and discover that inconsistency…
WIDER Face and Pedestrian Challenge 2018: Methods and Results
Chen Change Loy, Dahua Lin, Wanli Ouyang +49
This paper presents a review of the 2018 WIDER Challenge on Face and Pedestrian. The challenge focuses on the problem of precise localization of human faces and bodies, and accurat…
Super-Resolution with Deep Adaptive Image Resampling
Xu Jia, Hong Chang, Tinne Tuytelaars
Deep learning based methods have recently pushed the state-of-the-art on the problem of Single Image Super-Resolution (SISR). In this work, we revisit the more traditional interpol…