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Hongzhang Mu

3 accepted papers

2026

Fashion Microscope: Pixel-Level Attribute Perception via Optimal Transport and Neural Semantic Aggregation

AAAI 2026technical

Attribute-specific fashion retrieval aims to enhance fine-grained image retrieval by emphasizing the similarity of specific attributes. Current methods primarily rely on attention mechanisms to extract attribute-related visual features but face two key challenges: the limitations of coarse-grained l

Cited by 0SourcePDFScholar
2025

MAKAR: a Multi-Agent framework based Knowledge-Augmented Reasoning for Grounded Multimodal Named Entity Recognition

EMNLP 2025

Grounded Multimodal Named Entity Recognition (GMNER), which aims to extract textual entities, their types, and corresponding visual regions from image-text data, has become a critical task in multimodal information extraction. However, existing methods face two major challenges. First, they fail to

2024

Fine-Grained Features Alignment and Fusion for Text-Video Cross-Modal Retrieval

ICASSP 2024accepted

Text-video cross-modal retrieval is an increasingly prominent and challenging task that has garnered significant attention. Traditional models typically embed videos and texts into global vectors, aiming to capture the global features of these modalities. While the models often fall short in capturi…

Cited by 0SourceScholar