551 citations · 966 across the 62 of their papers we have counts for
7 papers · 1 filter
DeepSFM: Structure From Motion Via Deep Bundle Adjustment
Xingkui Wei, Yinda Zhang, Zhuwen Li +2
Structure from motion (SfM) is an essential computer vision problem which has not been well handled by deep learning. One of the promising trends is to apply explicit structural co…
Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition
Satoshi Tsutsui, Yanwei Fu, David Crandall
One-shot fine-grained visual recognition often suffers from the problem of training data scarcity for new fine-grained classes. To alleviate this problem, an off-the-shelf image ge…
Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformation
Chao Wen, Yinda Zhang, Zhuwen Li +1
We study the problem of shape generation in 3D mesh representation from a few color images with known camera poses. While many previous works learn to hallucinate the shape directl…
A Fine-Grained Facial Expression Database for End-to-End Multi-Pose Facial Expression Recognition
Wenxuan Wang, Qiang Sun, Tao Chen +5
The recent research of facial expression recognition has made a lot of progress due to the development of deep learning technologies, but some typical challenging problems such as…
Image Deformation Meta-Networks for One-Shot Learning
Zitian Chen, Yanwei Fu, Yu-Xiong Wang +3
Humans can robustly learn novel visual concepts even when images undergo various deformations and lose certain information. Mimicking the same behavior and synthesizing deformed in…
-LBI: Stochastic Split Linearized Bregman Iterations for Parsimonious Deep Learning
Yanwei Fu, Donghao Li, Xinwei Sun +3
This paper proposes a novel Stochastic Split Linearized Bregman Iteration (-LBI) algorithm to efficiently train the deep network. The -LBI introduces an iterative reg…