Qingchen Zhang

Prototype Matters: Domain-Wise Prototype Induction for Zero-Shot Recognition.

Zero-Shot Learning (ZSL) has achieved remarkable progress, yet most existing methods still rely heavily on predefined semantic embeddings as supervisory signals. Such semantics are often incomplete, inaccurate, and insufficiently discriminative, resulting in ambiguous decision boundaries and limited generalization to u...

ProST: an image prompt-guided multimodal representation learning framework for spatial domain identification

Abstract Motivation Spatial transcriptomics (ST) enables gene expression profiling while preserving the spatial organization of tissues, providing a powerful tool for dissecting tissue architecture and cellular heterogeneity. However, existing methods tend to prioritize improvements in clustering performance and overlo...

PRIME: A Decoupled Multi-agent Actor-Critic for Multi-view Clustering

A decoupled multi-agent actor-critic (PRIME) is proposed via defining multi-view clustering as a partially observable Markov game, which establishes a dynamic trial-and-error assignment process of samples in a decentralized perception with centralized learning framework to adaptively learn the optimal clustering strate...

DAHGT-CCI: Dynamic Heterogeneous Graph Transformer for Spatial Transcriptomics Cell-Cell Interaction Inference

It is demonstrated that DAHGT-CCI can more accurately reconstruct cell communication networks in complex tissue microenvironments, offering an indispensable computational tool for studying developmental processes, disease mechanisms, and potential therapeutic targets from a spatially resolved perspective.

3D reconstruction of spatial transcriptomics data with dual incremental deep reinforcement learning