83 citations · 305 across the 33 of their papers we have counts for
11 papers · 1 filter
Language-Assisted 3D Feature Learning for Semantic Scene Understanding
Junbo Zhang, Guofan Fan, Guanghan Wang +3
Learning descriptive 3D features is crucial for understanding 3D scenes with diverse objects and complex structures. However, it is usually unknown whether important geometric attr…
Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?
Runpei Dong, Zekun Qi, Linfeng Zhang +5
The success of deep learning heavily relies on large-scale data with comprehensive labels, which is more expensive and time-consuming to fetch in 3D compared to 2D images or natura…
Structured Knowledge Distillation Towards Efficient and Compact Multi-View 3D Detection
Linfeng Zhang, Yukang Shi, Hung-Shuo Tai +4
Detecting 3D objects from multi-view images is a fundamental problem in 3D computer vision. Recently, significant breakthrough has been made in multi-view 3D detection tasks. Howev…
LW-ISP: A Lightweight Model with ISP and Deep Learning
Hongyang Chen, Kaisheng Ma
The deep learning (DL)-based methods of low-level tasks have many advantages over the traditional camera in terms of hardware prospects, error accumulation and imaging effects. Rec…
Contrastive Deep Supervision
Linfeng Zhang, Xin Chen, Junbo Zhang +2
The success of deep learning is usually accompanied by the growth in neural network depth. However, the traditional training method only supervises the neural network at its last l…
Rethinking the Augmentation Module in Contrastive Learning: Learning Hierarchical Augmentation Invariance with Expanded Views
Junbo Zhang, Kaisheng Ma
A data augmentation module is utilized in contrastive learning to transform the given data example into two views, which is considered essential and irreplaceable. However, the pre…
Region-aware Knowledge Distillation for Efficient Image-to-Image Translation
Linfeng Zhang, Xin Chen, Runpei Dong +1
Recent progress in image-to-image translation has witnessed the success of generative adversarial networks (GANs). However, GANs usually contain a huge number of parameters, which…
PointDistiller: Structured Knowledge Distillation Towards Efficient and Compact 3D Detection
Linfeng Zhang, Runpei Dong, Hung-Shuo Tai +1
The remarkable breakthroughs in point cloud representation learning have boosted their usage in real-world applications such as self-driving cars and virtual reality. However, thes…
Unsupervised Deep Learning Meets Chan-Vese Model
Dihan Zheng, Chenglong Bao, Zuoqiang Shi +2
The Chan-Vese (CV) model is a classic region-based method in image segmentation. However, its piecewise constant assumption does not always hold for practical applications. Many im…
Learn from Unpaired Data for Image Restoration: A Variational Bayes Approach
Dihan Zheng, Xiaowen Zhang, Kaisheng Ma +1
Collecting paired training data is difficult in practice, but the unpaired samples broadly exist. Current approaches aim at generating synthesized training data from unpaired sampl…
Wavelet Knowledge Distillation: Towards Efficient Image-to-Image Translation
Linfeng Zhang, Xin Chen, Xiaobing Tu +3
Remarkable achievements have been attained with Generative Adversarial Networks (GANs) in image-to-image translation. However, due to a tremendous amount of parameters, state-of-th…