most citedComplex Background Subtraction by Pursuing Dynamic Spatio-Temporal Models

75 citations · 138 across the 4 of their papers we have counts for

collaborators

7 papers

cs.CV2016

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…

cs.CV2016

Attentive Contexts for Object Detection

Jianan Li, Yunchao Wei, Xiaodan Liang +4

Modern deep neural network based object detection methods typically classify candidate proposals using their interior features. However, global and local surrounding contexts that…

cs.CV2016

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…

cs.CV201524 cited

Matching-CNN Meets KNN: Quasi-Parametric Human Parsing

Si Liu, Xiaodan Liang, Luoqi Liu +6

Both parametric and non-parametric approaches have demonstrated encouraging performances in the human parsing task, namely segmenting a human image into several semantic regions (e…

cs.CV201512 cited

Recognizing Focal Liver Lesions in Contrast-Enhanced Ultrasound with Discriminatively Trained Spatio-Temporal Model

Xiaodan Liang, Qingxing Cao, Rui Huang +1

The aim of this study is to provide an automatic computational framework to assist clinicians in diagnosing Focal Liver Lesions (FLLs) in Contrast-Enhancement Ultrasound (CEUS). We…

cs.CV201575 cited

Complex Background Subtraction by Pursuing Dynamic Spatio-Temporal Models

Liang Lin, Yuanlu Xu, Xiaodan Liang +1

Although it has been widely discussed in video surveillance, background subtraction is still an open problem in the context of complex scenarios, e.g., dynamic backgrounds, illumin…