14 |
|
MemNet: A Persistent Memory Network for Image Restoration |
Ying Tai, Jian Yang, Xiaoming Liu, Chunyan Xu |
ICCV 2017 |
paper github |
13 |
|
Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs |
Vishwanath A. Sindagi and Vishal M. Patel |
ICCV 2017 |
paper |
12 |
|
UberNet: Training a `Universal' Convolutional Neural Network for Low-, Mid-, and High-Level Vision using Diverse Datasets and Limited Memory |
Iasonas Kokkinos |
CVPR 2017 |
paper code |
11 |
|
Few-Shot Object Recognition from Machine-Labeled Web Images |
Zhongwen Xu, Linchao Zhu, Yi Yang |
CVPR 2017 |
paper |
10 |
|
Full Resolution Image Compression with Recurrent Neural Networks |
George Toderici, Damien Vincent, Nick Johnston, Sung Jin Hwang, David Minnen, Joel Shor, Michele Covell |
CVPR 2017 |
paper github |
9 |
|
Weakly Supervised Cascaded Convolutional Networks |
Ali Diba, Vivek Sharma, Ali Pazandeh, Hamed Pirsiavash, Luc Van Gool |
CVPR 2017 |
paper |
8 |
|
Annotating Object Instances with a Polygon-RNN |
Lluis Castrejon, Kaustav Kundu, Raquel Urtasun, Sanja Fidler |
CVPR 2017 |
paper ⭐ |
7 |
|
Detecting Visual Relationships with Deep Relational Networks |
Bo Dai, Yuqi Zhang, Dahua Lin |
CVPR 2017 |
paper github |
6 |
|
Semi-Supervised Deep Learning for Monocular Depth Map Prediction |
Yevhen Kuznietsov, Jörg Stückler, Bastian Leibe |
CVPR 2017 |
paper |
5 |
|
Learning Cross-Modal Deep Representations for Robust Pedestrian Detection |
Dan Xu, Wanli Ouyang, Elisa Ricci, Xiaogang Wang, Nicu Sebe |
CVPR 2017 |
paper |
4 |
|
Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation |
Dan Xu, Elisa Ricci, Wanli Ouyang, Xiaogang Wang, Nicu Sebe |
CVPR 2017 |
paper github ⭐ |
3 |
|
Quality Aware Network for Set to Set Recognition |
Yu Liu, Junjie Yan, Wanli Ouyang |
CVPR 2017 |
paper |
2 |
|
SRN:Side-output Residual Network for Object Symmetry Detection in the Wild |
Wei Ke, Jie Chen, Jianbin Jiao, Guoying Zhao and Qixiang Ye |
CVPR 2017 |
paper github ⭐ |
1 |
|
AnchorNet: A Weakly Supervised Network to Learn Geometry-sensitive Features For Semantic Matching |
David Novotny, DianeLarlus, Andrea Vedaldi |
CVPR 2017 |
paper |