Attribute-Based Person Search via Noisy Graph-Enhanced Learning

Multimodal-guided self-distillation for unified person search.

This work proposes a novel Multimodal-Guided Self-Distillation (MGSD) method for Unified Person Search that leverages multimodal textual descriptions and self-distillation to enhance pedestrian representation learning, and introduces three key innovations.

A Survey on Text-Based Person Search.

This work provides a systematic review of TBPS by formalizing the problem setting and presenting a structured taxonomy of representative methods along four key technical dimensions: external knowledge-based approaches that leverage semantic priors to alleviate the inherent modality gap between visual and textual data.

Interpretable Region-Attribute Graph Matching Framework for Structure-Aware and Explainable Content-Based Image Retrieval

The proposed IR-AGN model exhibits a higher mean Average Precision (mAP) of 95.19% on the Revisited Paris (RParis) dataset, when compared with the existing Multi-Layer Orientation Histogram (MLOH) model.

Multimodal graph-based fusion via image descriptions for few-shot open-set recognition.

A multimodal Graph-based Fusion (MGF) framework that learns visually grounded semantic representations to enhance FSOR performance and achieves superior open-set recognition and competitive closed-set classification performance.

Instance-Level Consistent Graph With Unsupervised Human Parts for Person Re-Identification

Aggregating Neighbor Embedding Projection and Rank-Based Manifold Learning for Image Retrieval

This paper proposes a framework that combines neighbor embedding projections with rank-based manifold learning through rank aggregation, which can improve retrieval effectiveness in several scenarios, particularly when the baseline representation struggles to achieve high precision values.