activity
20172024
most citedD-City: A Large-Scale Dashcam Video Dataset of Diverse Traffic Scenarios

40 citations · 114 across the 12 of their papers we have counts for

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Showing cs.CVShow all

16 papers · 1 filter

cs.CV202116 cited

Towards More Flexible and Accurate Object Tracking with Natural Language: Algorithms and Benchmark

Xiao Wang, Xiujun Shu, Zhipeng Zhang +4

Tracking by natural language specification is a new rising research topic that aims at locating the target object in the video sequence based on its language description. Compared…

cs.CV20202 cited

Visual Object Tracking by Segmentation with Graph Convolutional Network

Bo Jiang, Panpan Zhang, Lili Huang

Segmentation-based tracking has been actively studied in computer vision and multimedia. Superpixel based object segmentation and tracking methods are usually developed for this ta…

cs.CV2019

\emph{cm}SalGAN: RGB-D Salient Object Detection with Cross-View Generative Adversarial Networks

Bo Jiang, Zitai Zhou, Xiao Wang +2

Image salient object detection (SOD) is an active research topic in computer vision and multimedia area. Fusing complementary information of RGB and depth has been demonstrated to…

cs.CV20194 cited

AttKGCN: Attribute Knowledge Graph Convolutional Network for Person Re-identification

Bo Jiang, Xixi Wang, Jin Tang

Discriminative feature representation of person image is important for person re-identification (Re-ID) task. Recently, attributes have been demonstrated beneficially in guiding fo…

cs.CV20192 cited

GmCN: Graph Mask Convolutional Network

Bo Jiang, Beibei Wang, Jin Tang +1

Graph Convolutional Networks (GCNs) have shown very powerful for graph data representation and learning tasks. Existing GCNs usually conduct feature aggregation on a fixed neighbor…

cs.CV201923 cited

GLMNet: Graph Learning-Matching Networks for Feature Matching

Bo Jiang, Pengfei Sun, Jin Tang +1

Recently, graph convolutional networks (GCNs) have shown great potential for the task of graph matching. It can integrate graph node feature embedding, node-wise affinity learning…