28 citations · 56 across the 9 of their papers we have counts for
10 papers
EurNet: Efficient Multi-Range Relational Modeling of Spatial Multi-Relational Data
Minghao Xu, Yuanfan Guo, Yi Xu +3
Modeling spatial relationship in the data remains critical across many different tasks, such as image classification, semantic segmentation and protein structure understanding. Pre…
HIRL: A General Framework for Hierarchical Image Representation Learning
Minghao Xu, Yuanfan Guo, Xuanyu Zhu +5
Learning self-supervised image representations has been broadly studied to boost various visual understanding tasks. Existing methods typically learn a single level of image semant…
A Roadmap for Big Model
Sha Yuan, Hanyu Zhao, Shuai Zhao +97
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm. Researchers have achieved various outcomes in the c…
HCSC: Hierarchical Contrastive Selective Coding
Yuanfan Guo, Minghao Xu, Jiawen Li +4
Hierarchical semantic structures naturally exist in an image dataset, in which several semantically relevant image clusters can be further integrated into a larger cluster with coa…
Cross-category Video Highlight Detection via Set-based Learning
Minghao Xu, Hang Wang, Bingbing Ni +3
Autonomous highlight detection is crucial for enhancing the efficiency of video browsing on social media platforms. To attain this goal in a data-driven way, one may often face the…
Self-supervised Graph-level Representation Learning with Local and Global Structure
Minghao Xu, Hang Wang, Bingbing Ni +2
This paper studies unsupervised/self-supervised whole-graph representation learning, which is critical in many tasks such as molecule properties prediction in drug and material dis…