56 citations · 351 across the 33 of their papers we have counts for
8 papers · 1 filter
Siamese Cascaded Region Proposal Networks for Real-Time Visual Tracking
Heng Fan, Haibin Ling
Region proposal networks (RPN) have been recently combined with the Siamese network for tracking, and shown excellent accuracy with high efficiency. Nevertheless, previously propos…
Scene Parsing via Dense Recurrent Neural Networks with Attentional Selection
Heng Fan, Peng Chu, Longin Jan Latecki +1
Recurrent neural networks (RNNs) have shown the ability to improve scene parsing through capturing long-range dependencies among image units. In this paper, we propose dense RNNs f…
LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking
Heng Fan, Liting Lin, Fan Yang +7
In this paper, we present LaSOT, a high-quality benchmark for Large-scale Single Object Tracking. LaSOT consists of 1,400 sequences with more than 3.5M frames in total. Each frame…
Robust and Efficient Graph Correspondence Transfer for Person Re-identification
Qin Zhou, Heng Fan, Hua Yang +4
Spatial misalignment caused by variations in poses and viewpoints is one of the most critical issues that hinders the performance improvement in existing person re-identification (…
Graph Correspondence Transfer for Person Re-identification
Qin Zhou, Heng Fan, Shibao Zheng +4
In this paper, we propose a graph correspondence transfer (GCT) approach for person re-identification. Unlike existing methods, the GCT model formulates person re-identification as…
Weighted Bilinear Coding over Salient Body Parts for Person Re-identification
Zhigang Chang, Qin Zhou, Heng Fan +4
Deep convolutional neural networks (CNNs) have demonstrated dominant performance in person re-identification (Re-ID). Existing CNN based methods utilize global average pooling (GAP…
Parallel Tracking and Verifying
Heng Fan, Haibin Ling
Being intensively studied, visual object tracking has witnessed great advances in either speed (e.g., with correlation filters) or accuracy (e.g., with deep features). Real-time an…
Dense Recurrent Neural Networks for Scene Labeling
Heng Fan, Haibin Ling
Recently recurrent neural networks (RNNs) have demonstrated the ability to improve scene labeling through capturing long-range dependencies among image units. In this paper, we pro…