no code implementations • 24 Jan 2024 • Shan Yang, Yongfei Zhang
This paper will investigate how to adapt them for the task of ReID.
no code implementations • 11 Oct 2023 • Guiwei Zhang, Yongfei Zhang, Zichang Tan
In contrast, we find that some cross-modal correlated high-frequency components contain discriminative visual patterns and are less affected by variations such as wavelength, pose, and background clutter than holistic images.
no code implementations • CVPR 2023 • Guiwei Zhang, Yongfei Zhang, Tianyu Zhang, Bo Li, ShiLiang Pu
Although recent studies empirically show that injecting Convolutional Neural Networks (CNNs) into Vision Transformers (ViTs) can improve the performance of person re-identification, the rationale behind it remains elusive.
1 code implementation • ACL 2022 • Guanglin Niu, Bo Li, Yongfei Zhang, ShiLiang Pu
The previous knowledge graph embedding (KGE) techniques suffer from invalid negative sampling and the uncertainty of fact-view link prediction, limiting KGC's performance.
no code implementations • COLING 2022 • Guanglin Niu, Bo Li, Yongfei Zhang, ShiLiang Pu
Knowledge graph (KG) inference aims to address the natural incompleteness of KGs, including rule learning-based and KG embedding (KGE) models.
1 code implementation • ACL 2021 • Shan Yang, Yongfei Zhang, Guanglin Niu, Qinghua Zhao, ShiLiang Pu
Few-shot relation extraction (FSRE) is of great importance in long-tail distribution problem, especially in special domain with low-resource data.
no code implementations • 30 Mar 2021 • Tianyu Zhang, Longhui Wei, Lingxi Xie, Zijie Zhuang, Yongfei Zhang, Bo Li, Qi Tian
Recently, the Transformer module has been transplanted from natural language processing to computer vision.
1 code implementation • CVPR 2021 • Tianyu Zhang, Lingxi Xie, Longhui Wei, Zijie Zhuang, Yongfei Zhang, Bo Li, Qi Tian
The main difficulty of person re-identification (ReID) lies in collecting annotated data and transferring the model across different domains.
no code implementations • 6 Oct 2020 • Guanglin Niu, Bo Li, Yongfei Zhang, Yongpan Sheng, Chuan Shi, Jingyang Li, ShiLiang Pu
Inference on a large-scale knowledge graph (KG) is of great importance for KG applications like question answering.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Guanglin Niu, Bo Li, Yongfei Zhang, ShiLiang Pu, Jingyang Li
Recent advances in Knowledge Graph Embedding (KGE) allow for representing entities and relations in continuous vector spaces.
1 code implementation • 20 Nov 2019 • Guanglin Niu, Yongfei Zhang, Bo Li, Peng Cui, Si Liu, Jingyang Li, Xiaowei Zhang
Representation learning on a knowledge graph (KG) is to embed entities and relations of a KG into low-dimensional continuous vector spaces.
no code implementations • 15 Oct 2019 • Mingjie Wu, Yongfei Zhang, Tianyu Zhang, Wen-qi Zhang
Vehicle re-identification (Re-ID) is very important in intelligent transportation and video surveillance. Prior works focus on extracting discriminative features from visual appearance of vehicles or using visual-spatio-temporal information. However, background interference in vehicle re-identification have not been explored. In the actual large-scale spatio-temporal scenes, the same vehicle usually appears in different backgrounds while different vehicles might appear in the same background, which will seriously affect the re-identification performance.
1 code implementation • 24 Sep 2019 • Tianyu Zhang, Lingxi Xie, Longhui Wei, Yongfei Zhang, Bo Li, Qi Tian
Differently, this paper investigates ReID in an unexplored single-camera-training (SCT) setting, where each person in the training set appears in only one camera.