132 citations · 495 across the 18 of their papers we have counts for
22 papers
Guided Point Contrastive Learning for Semi-supervised Point Cloud Semantic Segmentation
Li Jiang, Shaoshuai Shi, Zhuotao Tian +4
Rapid progress in 3D semantic segmentation is inseparable from the advances of deep network models, which highly rely on large-scale annotated data for training. To address the hig…
Deep Structured Instance Graph for Distilling Object Detectors
Yixin Chen, Pengguang Chen, Shu Liu +2
Effectively structuring deep knowledge plays a pivotal role in transfer from teacher to student, especially in semantic vision tasks. In this paper, we present a simple knowledge s…
Exploring and Improving Mobile Level Vision Transformers
Pengguang Chen, Yixin Chen, Shu Liu +2
We study the vision transformer structure in the mobile level in this paper, and find a dramatic performance drop. We analyze the reason behind this phenomenon, and propose a novel…
Parametric Contrastive Learning
Jiequan Cui, Zhisheng Zhong, Shu Liu +2
In this paper, we propose Parametric Contrastive Learning (PaCo) to tackle long-tailed recognition. Based on theoretical analysis, we observe supervised contrastive loss tends to b…
Semi-supervised Semantic Segmentation with Directional Context-aware Consistency
Xin Lai, Zhuotao Tian, Li Jiang +4
Semantic segmentation has made tremendous progress in recent years. However, satisfying performance highly depends on a large number of pixel-level annotations. Therefore, in this…
Distilling Knowledge via Knowledge Review
Pengguang Chen, Shu Liu, Hengshuang Zhao +1
Knowledge distillation transfers knowledge from the teacher network to the student one, with the goal of greatly improving the performance of the student network. Previous methods…