Shiping Wang

Multi-Modal Hierarchical Retrieval-Augmented Generation for Document Question Answering

The Document Question Answering (DocQA) task necessitates the synergistic interpretation of visual and textual information embedded within documents. Although Retrieval-Augmented Generation (RAG) has enhanced the capabilities of Large Vision-Language Models (LVLMs), existing approaches still encounter significant bottl...

Multi-View Alignment and Denoising via Center-Guided Spectral Diffusion

Center-guided spectral diffusion is proposed, which replaces traditional alignment with generative modeling and outperforms state-of-the-art methods on several datasets and alleviates the noise amplification problem commonly found in traditional alignment methods.

McGCN: Learning Continuous Graph Dynamics for Multi-Channel Fusion

IRPP: Invariant Representation Learning With Progressive Prototype Refinement for Unsupervised Person Re-Identification

Unsupervised person re-identification (USL-ReID) typically relies on clustering to generate pseudo-labels, but significant cross-view appearance variations often cause images of the same identity to be split into different clusters. Training on such noisy pseudo-labels severely degrades the learned representations. The...

Multi-Modal Hierarchical Retrieval-Augmented Generation for Document Question Answering

This work introduces MMHRAG, a novel Multi-Modal Hierarchical Retrieval-Augmented Generation framework that achieves cross-modal interaction on DocQA for the first time, and designs a Summarizing Agent to resolve logical conflicts, information redundancy, and granularity discrepancies among retrieved cross-modal eviden...