activity
20192022
most citedRecent Advances on HEVC Inter-frame Coding: From Optimization to Implementation and Beyond

25 citations · 48 across the 8 of their papers we have counts for

collaborators

8 papers

cs.AI20221 cited

CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion

Guanglin Niu, Bo Li, Yongfei Zhang +1

Knowledge graphs store a large number of factual triples while they are still incomplete, inevitably. The previous knowledge graph completion (KGC) models predict missing links bet…

cs.CV20205 cited

UnrealPerson: An Adaptive Pipeline towards Costless Person Re-identification

Tianyu Zhang, Lingxi Xie, Longhui Wei +4

The main difficulty of person re-identification (ReID) lies in collecting annotated data and transferring the model across different domains. This paper presents UnrealPerson, a no…

cs.AI20204 cited

Joint Semantics and Data-Driven Path Representation for Knowledge Graph Inference

Guanglin Niu, Bo Li, Yongfei Zhang +4

Inference on a large-scale knowledge graph (KG) is of great importance for KG applications like question answering. The path-based reasoning models can leverage much information ov…

cs.CL20206 cited

AutoETER: Automated Entity Type Representation for Knowledge Graph Embedding

Guanglin Niu, Bo Li, Yongfei Zhang +2

Recent advances in Knowledge Graph Embedding (KGE) allow for representing entities and relations in continuous vector spaces. Some traditional KGE models leveraging additional type…

cs.CL20197 cited

Rule-Guided Compositional Representation Learning on Knowledge Graphs

Guanglin Niu, Yongfei Zhang, Bo Li +4

Representation learning on a knowledge graph (KG) is to embed entities and relations of a KG into low-dimensional continuous vector spaces. Early KG embedding methods only pay atte…

cs.CV2019

Background Segmentation for Vehicle Re-Identification

Mingjie Wu, Yongfei Zhang, Tianyu Zhang +1

Vehicle re-identification (Re-ID) is very important in intelligent transportation and video surveillance.Prior works focus on extracting discriminative features from visual appeara…