5 citations · 13 across the 5 of their papers we have counts for
9 papers
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…
Boosted Dynamic Neural Networks
Haichao Yu, Haoxiang Li, Gang Hua +2
Early-exiting dynamic neural networks (EDNN), as one type of dynamic neural networks, has been widely studied recently. A typical EDNN has multiple prediction heads at different la…
Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking
Yiding Yang, Zhou Ren, Haoxiang Li +3
Multi-person pose estimation and tracking serve as crucial steps for video understanding. Most state-of-the-art approaches rely on first estimating poses in each frame and only the…
Semi-supervised Long-tailed Recognition using Alternate Sampling
Bo Liu, Haoxiang Li, Hao Kang +2
Main challenges in long-tailed recognition come from the imbalanced data distribution and sample scarcity in its tail classes. While techniques have been proposed to achieve a more…
GistNet: a Geometric Structure Transfer Network for Long-Tailed Recognition
Bo Liu, Haoxiang Li, Hao Kang +2
The problem of long-tailed recognition, where the number of examples per class is highly unbalanced, is considered. It is hypothesized that the well known tendency of standard clas…
Breadcrumbs: Adversarial Class-Balanced Sampling for Long-tailed Recognition
Bo Liu, Haoxiang Li, Hao Kang +2
The problem of long-tailed recognition, where the number of examples per class is highly unbalanced, is considered. While training with class-balanced sampling has been shown effec…