619 citations · 3.7k across the 114 of their papers we have counts for
10 papers · 1 filter
Reversible Recursive Instance-level Object Segmentation
Xiaodan Liang, Yunchao Wei, Xiaohui Shen +4
In this work, we propose a novel Reversible Recursive Instance-level Object Segmentation (R2-IOS) framework to address the challenging instance-level object segmentation task. R2-I…
Semantic Object Parsing with Local-Global Long Short-Term Memory
Xiaodan Liang, Xiaohui Shen, Donglai Xiang +3
Semantic object parsing is a fundamental task for understanding objects in detail in computer vision community, where incorporating multi-level contextual information is critical f…
Scale-aware Fast R-CNN for Pedestrian Detection
Jianan Li, Xiaodan Liang, ShengMei Shen +3
In this work, we consider the problem of pedestrian detection in natural scenes. Intuitively, instances of pedestrians with different spatial scales may exhibit dramatically differ…
Proposal-free Network for Instance-level Object Segmentation
Xiaodan Liang, Yunchao Wei, Xiaohui Shen +3
Instance-level object segmentation is an important yet under-explored task. The few existing studies are almost all based on region proposal methods to extract candidate segments a…
STC: A Simple to Complex Framework for Weakly-supervised Semantic Segmentation
Yunchao Wei, Xiaodan Liang, Yunpeng Chen +5
Recently, significant improvement has been made on semantic object segmentation due to the development of deep convolutional neural networks (DCNNs). Training such a DCNN usually r…
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…