8 papers
Dual-Space Modality Consistency Learning for Universal Cross-Modal Re-Identification
Yujian Zhao, Yukang Zhao, Hankun Liu +4
Cross-modal Re-Identification (ReID) aims to retrieve the same identity across heterogeneous imaging modalities and has been widely studied in visible-infrared person ReID and cros…
RADD: Retrieval-Anchored Discrete Diffusion for Multi-Modal Knowledge Graph Completion
Guanglin Niu, Bo Li
Multi-modal knowledge graph completion aims to infer missing links by integrating structural, visual, and textual information. However, global retrieval can leave fine-grained enti…
Tokenization, Fusion and Decoupling: Bridging the Granularity Mismatch Between Large Language Models and Knowledge Graphs
Siyue Su, Jian Yang, Bo Li +1
Leveraging Large Language Models (LLMs) for Knowledge Graph Completion (KGC) is promising but hindered by a fundamental granularity mismatch. LLMs operate on fragmented token seque…
Modality-Transition Representation Learning for Visible-Infrared Person Re-Identification
Chao Yuan, Zanwu Liu, Guiwei Zhang +4
Visible-infrared person re-identification (VI-ReID) technique could associate the pedestrian images across visible and infrared modalities in the practical scenarios of background…
Resolution as a Direction: Vector-Panning Feature Alignment for Cross-Resolution Re-Identification
Zanwu Liu, Chao Yuan, Bo Li +2
Cross-resolution person re-identification (CR-ReID) remains challenging in practical surveillance, where camera quality and capture distance lead to substantial resolution gaps bet…
A Survey of Task-Oriented Knowledge Graph Reasoning: Status, Applications, and Prospects
Guanglin Niu, Bo Li, Yangguang Lin
Knowledge graphs (KGs) have emerged as a powerful paradigm for structuring and leveraging diverse real-world knowledge, which serve as a fundamental technology for enabling cogniti…
Identity-aware Feature Decoupling Learning for Clothing-change Person Re-identification
Haoxuan Xu, Bo Li, Guanglin Niu
Clothing-change person re-identification (CC Re-ID) has attracted increasing attention in recent years due to its application prospect. Most existing works struggle to adequately e…
A Pluggable Common Sense-Enhanced Framework for Knowledge Graph Completion
Guanglin Niu, Bo Li, Siling Feng
Knowledge graph completion (KGC) tasks aim to infer missing facts in a knowledge graph (KG) for many knowledge-intensive applications. However, existing embedding-based KGC approac…