activity
20162021
most citedDeep Stereo Matching with Explicit Cost Aggregation Sub-Architecture

21 citations · 60 across the 10 of their papers we have counts for

collaborators

12 papers

cs.CV20211 cited

A Decomposition Model for Stereo Matching

Chengtang Yao, Yunde Jia, Huijun Di +2

In this paper, we present a decomposition model for stereo matching to solve the problem of excessive growth in computational cost (time and memory cost) as the resolution increase…

cs.LG20215 cited

A Hyperbolic-to-Hyperbolic Graph Convolutional Network

Jindou Dai, Yuwei Wu, Zhi Gao +1

Hyperbolic graph convolutional networks (GCNs) demonstrate powerful representation ability to model graphs with hierarchical structure. Existing hyperbolic GCNs resort to tangent s…

cs.CV20201 cited

Video Captioning Using Weak Annotation

Jingyi Hou, Yunde Jia, Xinxiao wu +1

Video captioning has shown impressive progress in recent years. One key reason of the performance improvements made by existing methods lie in massive paired video-sentence data, b…

cs.CV20205 cited

Content-Aware Inter-Scale Cost Aggregation for Stereo Matching

Chengtang Yao, Yunde Jia, Huijun Di +2

Cost aggregation is a key component of stereo matching for high-quality depth estimation. Most methods use multi-scale processing to downsample cost volume for proper context infor…

cs.CV20202 cited

Deep 3D Portrait from a Single Image

Sicheng Xu, Jiaolong Yang, Dong Chen +4

In this paper, we present a learning-based approach for recovering the 3D geometry of human head from a single portrait image. Our method is learned in an unsupervised manner witho…

cs.CV201913 cited

Relational Reasoning using Prior Knowledge for Visual Captioning

Jingyi Hou, Xinxiao Wu, Yayun Qi +3

Exploiting relationships among objects has achieved remarkable progress in interpreting images or videos by natural language. Most existing methods resort to first detecting object…