3 citations · 8 across the 5 of their papers we have counts for
5 papers
CPCM: Contextual Point Cloud Modeling for Weakly-supervised Point Cloud Semantic Segmentation
Lizhao Liu, Zhuangwei Zhuang, Shangxin Huang +5
We study the task of weakly-supervised point cloud semantic segmentation with sparse annotations (e.g., less than 0.1% points are labeled), aiming to reduce the expensive cost of d…
TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities
Zhe Zhao, Yudong Li, Cheng Hou +23
Recently, the success of pre-training in text domain has been fully extended to vision, audio, and cross-modal scenarios. The proposed pre-training models of different modalities a…
Contrastive Credibility Propagation for Reliable Semi-Supervised Learning
Brody Kutt, Pralay Ramteke, Xavier Mignot +6
Producing labels for unlabeled data is error-prone, making semi-supervised learning (SSL) troublesome. Often, little is known about when and why an algorithm fails to outperform a…
Instance Segmentation for Chinese Character Stroke Extraction, Datasets and Benchmarks
Lizhao Liu, Kunyang Lin, Shangxin Huang +4
Stroke is the basic element of Chinese character and stroke extraction has been an important and long-standing endeavor. Existing stroke extraction methods are often handcrafted an…
DAS: Densely-Anchored Sampling for Deep Metric Learning
Lizhao Liu, Shangxin Huang, Zhuangwei Zhuang +3
Deep Metric Learning (DML) serves to learn an embedding function to project semantically similar data into nearby embedding space and plays a vital role in many applications, such…