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14 papers · 2 filters
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…
Dense Human Body Correspondences Using Convolutional Networks
Lingyu Wei, Qixing Huang, Duygu Ceylan +2
We propose a deep learning approach for finding dense correspondences between 3D scans of people. Our method requires only partial geometric information in the form of two depth ma…
Learning a Discriminative Model for the Perception of Realism in Composite Images
Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman +1
What makes an image appear realistic? In this work, we are answering this question from a data-driven perspective by learning the perception of visual realism directly from large a…
Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks
Quanzeng You, Jiebo Luo, Hailin Jin +1
Sentiment analysis of online user generated content is important for many social media analytics tasks. Researchers have largely relied on textual sentiment analysis to develop sys…
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…