9.8k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- California Institute of TechnologyUS101 papers
- Massachusetts Institute of TechnologyUS100 papers
- Columbia UniversityUS97 papers
- Monash UniversityAU97 papers
- Stanford UniversityUS97 papers
- University of MichiganUS97 papers
- University of MinnesotaUS97 papers
- Australian National UniversityAU96 papers
- California State University, FullertonUS96 papers
- Cardiff UniversityGB96 papers
- Leibniz University HannoverDE96 papers
- LIGO Scientific CollaborationUS96 papers
6 papers · 1 filter
Glissando-Net: Deep sinGLe vIew category level poSe eStimation ANd 3D recOnstruction
Bo Sun, Hao Kang, Li Guan +3
We present a deep learning model, dubbed Glissando-Net, to simultaneously estimate the pose and reconstruct the 3D shape of objects at the category level from a single RGB image. P…
Image-GS: Content-Adaptive Image Representation via 2D Gaussians
Yunxiang Zhang, Bingxuan Li, Alexandr Kuznetsov +6
Neural image representations have emerged as a promising approach for encoding and rendering visual data. Combined with learning-based workflows, they demonstrate impressive trade-…
Recurrent Aligned Network for Generalized Pedestrian Trajectory Prediction
Yonghao Dong, Le Wang, Sanping Zhou +2
Pedestrian trajectory prediction is a crucial component in computer vision and robotics, but remains challenging due to the domain shift problem. Previous studies have tried to tac…
Taming Reversible Halftoning via Predictive Luminance
Cheuk-Kit Lau, Menghan Xia, Tien-Tsin Wong
Traditional halftoning usually drops colors when dithering images with binary dots, which makes it difficult to recover the original color information. We proposed a novel halftoni…
A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels
Yifan Ding, Liqiang Wang, Deliang Fan +1
The recent success of deep neural networks is powered in part by large-scale well-labeled training data. However, it is a daunting task to laboriously annotate an ImageNet-like dat…
Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effect
Xiang Wei, Boqing Gong, Zixia Liu +2
Despite being impactful on a variety of problems and applications, the generative adversarial nets (GANs) are remarkably difficult to train. This issue is formally analyzed by \cit…