59 citations · 278 across the 18 of their papers we have counts for
18 papers
A Systematic Study of Joint Representation Learning on Protein Sequences and Structures
Zuobai Zhang, Chuanrui Wang, Minghao Xu +4
Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein functions. Recent sequence representation learning methods based…
Pre-Training Protein Encoder via Siamese Sequence-Structure Diffusion Trajectory Prediction
Zuobai Zhang, Minghao Xu, Aurélie Lozano +3
Self-supervised pre-training methods on proteins have recently gained attention, with most approaches focusing on either protein sequences or structures, neglecting the exploration…
ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts
Minghao Xu, Xinyu Yuan, Santiago Miret +1
Current protein language models (PLMs) learn protein representations mainly based on their sequences, thereby well capturing co-evolutionary information, but they are unable to exp…
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…
PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding
Minghao Xu, Zuobai Zhang, Jiarui Lu +5
We are now witnessing significant progress of deep learning methods in a variety of tasks (or datasets) of proteins. However, there is a lack of a standard benchmark to evaluate th…
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…
Spotlights: Probing Shapes from Spherical Viewpoints
Jiaxin Wei, Lige Liu, Ran Cheng +6
Recent years have witnessed the surge of learned representations that directly build upon point clouds. Though becoming increasingly expressive, most existing representations still…
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…
Protein Representation Learning by Geometric Structure Pretraining
Zuobai Zhang, Minghao Xu, Arian Jamasb +4
Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein function or structure. Existing approaches usually pretrain prote…
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…
PIT: Position-Invariant Transform for Cross-FoV Domain Adaptation
Qiqi Gu, Qianyu Zhou, Minghao Xu +5
Cross-domain object detection and semantic segmentation have witnessed impressive progress recently. Existing approaches mainly consider the domain shift resulting from external en…
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…
Graphical Modeling for Multi-Source Domain Adaptation
Minghao Xu, Hang Wang, Bingbing Ni
Multi-Source Domain Adaptation (MSDA) focuses on transferring the knowledge from multiple source domains to the target domain, which is a more practical and challenging problem com…
Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation
Hang Wang, Minghao Xu, Bingbing Ni +1
Transferring knowledges learned from multiple source domains to target domain is a more practical and challenging task than conventional single-source domain adaptation. Furthermor…
Cross-domain Detection via Graph-induced Prototype Alignment
Minghao Xu, Hang Wang, Bingbing Ni +2
Applying the knowledge of an object detector trained on a specific domain directly onto a new domain is risky, as the gap between two domains can severely degrade model's performan…
Adversarial Domain Adaptation with Domain Mixup
Minghao Xu, Jian Zhang, Bingbing Ni +4
Recent works on domain adaptation reveal the effectiveness of adversarial learning on filling the discrepancy between source and target domains. However, two common limitations exi…
Learning Context Graph for Person Search
Yichao Yan, Qiang Zhang, Bingbing Ni +3
Person re-identification has achieved great progress with deep convolutional neural networks. However, most previous methods focus on learning individual appearance feature embeddi…