4 citations · 6 across the 11 of their papers we have counts for
11 papers
PHA-Net: Prototype-based Hierarchical Alignment Network for Text-Video Retrieval
Xiaolun Jing, Kezhao Yin, Xinxing Yang +2
With the emergence of large-scale image-text pre-training models, e.g., CLIP, text-video retrieval has experienced substantial advances in recent years. Existing best-performing me…
StarCodex: Dynamic Coding Harness for Starlink Measurement Analysis and Experiment Automation
Pengcheng Luo, Zhiming Shao, Bowen Zhang +2
Starlink and other low Earth orbit (LEO) satellite broadband systems are producing increasingly diverse measurement data across regions, time periods, and access conditions. These…
TC-MGC: Text-Conditioned Multi-Grained Contrastive Learning for Text-Video Retrieval
Xiaolun Jing, Genke Yang, Jian Chu
Motivated by the success of coarse-grained or fine-grained contrast in text-video retrieval, there emerge multi-grained contrastive learning methods which focus on the integration…
ALADIN-: A Distributed Optimization Algorithm for Solving MPCC Problems
Yifei Wang, Shuting Wu, Genke Yang +3
Mathematical Programs with Complementarity Constraints (MPCC) are critical in various real-world applications but notoriously challenging due to non-smoothness and degeneracy from…
An Empirical Study of Excitation and Aggregation Design Adaptions in CLIP4Clip for Video-Text Retrieval
Xiaolun Jing, Genke Yang, Jian Chu
CLIP4Clip model transferred from the CLIP has been the de-factor standard to solve the video clip retrieval task from frame-level input, triggering the surge of CLIP4Clip-based mod…
ALNSynergy: a graph convolutional network with multi-representation alignment for drug synergy prediction
Xinxing Yang, Jiachen Li, Xiao Kang +4
Drug combination refers to the use of two or more drugs to treat a specific disease at the same time. It is currently the mainstream way to treat complex diseases. Compared with si…
GraphCL-DTA: a graph contrastive learning with molecular semantics for drug-target binding affinity prediction
Xinxing Yang, Genke Yang, Jian Chu
Drug-target binding affinity prediction plays an important role in the early stages of drug discovery, which can infer the strength of interactions between new drugs and new target…
The Balanced Matrix Factorization for Computational Drug Repositioning
Xinxing Yang, Genke Yang, Jian Chu
Computational drug repositioning aims to discover new uses of drugs that have been marketed. However, the existing models suffer from the following limitations. Firstly, in the rea…
Self-supervised Learning for Label Sparsity in Computational Drug Repositioning
Xinxing Yang, Genke Yang, Jian Chu
The computational drug repositioning aims to discover new uses for marketed drugs, which can accelerate the drug development process and play an important role in the existing drug…
The Computational Drug Repositioning without Negative Sampling
Xinxing Yang, Genke Yang, Jian Chu
Computational drug repositioning technology is an effective tool to accelerate drug development. Although this technique has been widely used and successful in recent decades, many…
The Neural Metric Factorization for Computational Drug Repositioning
Xinxing Yang, Genke Yangand Jian Chu
Computational drug repositioning aims to discover new therapeutic diseases for marketed drugs and has the advantages of low cost, short development cycle, and high controllability…