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Fujian Normal University

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Research library13linked papers
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Selected work

Representative Papers

OneEmo: A Unified Multimodal Reasoning Model for Emotion Perception, Understanding, and Interaction

Aug 06, 2026

This work addresses the limitation of existing affective intelligence models, which are typically confined to single tasks and thus fail to exploit cross-task synergies. To overcome this, we propose OneEmo, a unified general-purpose emotional intelligence model that integrates emotion perception, understanding, and interaction through multi-task joint learning. Our key contributions include the construction of EmoWorld-130K, a large-scale multi-task dataset; the design of Emo-Chord, a reinforcement learning strategy that explicitly shares reasoning trajectories across tasks; and a unified optimization framework combining human feedback distillation, supervised fine-tuning, and a shared reward mechanism. Experimental results demonstrate that OneEmo outperforms same-scale baselines on most benchmarks and achieves performance comparable to commercial systems with significantly fewer parameters.

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LGD-Net: Latent-Guided Dual-Stream Network for HER2 Scoring with Task-Specific Domain Knowledge

Feb 19, 2026

This work proposes LGD-Net, a novel framework for predicting HER2 status directly from hematoxylin and eosin (H&E)-stained whole-slide images without explicitly generating virtual immunohistochemistry (IHC) images. Addressing the high cost and resource dependency of conventional HER2 IHC testing—and circumventing the computational burden and reconstruction artifacts associated with pixel-level virtual staining—LGD-Net leverages a cross-modal feature hallucination mechanism to map H&E morphological features into the latent space of IHC molecular representations. The architecture integrates teacher-guided distillation, a dual-stream design, and lightweight, domain knowledge–driven auxiliary tasks (e.g., nuclear distribution and membrane staining intensity) to enhance both discriminative power and interpretability. Evaluated on the BCI dataset, the method achieves state-of-the-art HER2 scoring performance using only H&E inputs, significantly outperforming existing baselines.

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Dual Refinement Cycle Learning: Unsupervised Text Classification of Mamba and Community Detection on Text Attributed Graph

Dec 07, 2025

To address the performance degradation in community detection and text classification on text-attributed graphs caused by label scarcity, this paper proposes a structural-semantic dual-refinement cyclic learning framework. The method introduces a novel bidirectional co-optimization mechanism between a GCN-based Community Detection Module (GCN-CDM) and a Text Semantic Modeling Module (TSMM), enabling unsupervised joint enhancement of graph structure and textual semantics via iterative pseudo-labeling. Furthermore, it integrates community signals into the Mamba architecture to construct the first annotation-free, graph-guided generative text classifier. Evaluated on multiple benchmark datasets, the approach significantly improves both structural cohesion and semantic consistency of detected communities. Remarkably, the Mamba classifier trained solely on community signals achieves accuracy comparable to fully supervised baselines, effectively bridging the gap between unsupervised graph representation learning and downstream text understanding.

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Recent publications

Latest Papers

OneEmo: A Unified Multimodal Reasoning Model for Emotion Perception, Understanding, and Interaction

Aug 06, 2026

This work addresses the limitation of existing affective intelligence models, which are typically confined to single tasks and thus fail to exploit cross-task synergies. To overcome this, we propose OneEmo, a unified general-purpose emotional intelligence model that integrates emotion perception, understanding, and interaction through multi-task joint learning. Our key contributions include the construction of EmoWorld-130K, a large-scale multi-task dataset; the design of Emo-Chord, a reinforcement learning strategy that explicitly shares reasoning trajectories across tasks; and a unified optimization framework combining human feedback distillation, supervised fine-tuning, and a shared reward mechanism. Experimental results demonstrate that OneEmo outperforms same-scale baselines on most benchmarks and achieves performance comparable to commercial systems with significantly fewer parameters.

0 citationsRead paper

LGD-Net: Latent-Guided Dual-Stream Network for HER2 Scoring with Task-Specific Domain Knowledge

Feb 19, 2026

This work proposes LGD-Net, a novel framework for predicting HER2 status directly from hematoxylin and eosin (H&E)-stained whole-slide images without explicitly generating virtual immunohistochemistry (IHC) images. Addressing the high cost and resource dependency of conventional HER2 IHC testing—and circumventing the computational burden and reconstruction artifacts associated with pixel-level virtual staining—LGD-Net leverages a cross-modal feature hallucination mechanism to map H&E morphological features into the latent space of IHC molecular representations. The architecture integrates teacher-guided distillation, a dual-stream design, and lightweight, domain knowledge–driven auxiliary tasks (e.g., nuclear distribution and membrane staining intensity) to enhance both discriminative power and interpretability. Evaluated on the BCI dataset, the method achieves state-of-the-art HER2 scoring performance using only H&E inputs, significantly outperforming existing baselines.

0 citationsRead paper

Dual Refinement Cycle Learning: Unsupervised Text Classification of Mamba and Community Detection on Text Attributed Graph

Dec 07, 2025

To address the performance degradation in community detection and text classification on text-attributed graphs caused by label scarcity, this paper proposes a structural-semantic dual-refinement cyclic learning framework. The method introduces a novel bidirectional co-optimization mechanism between a GCN-based Community Detection Module (GCN-CDM) and a Text Semantic Modeling Module (TSMM), enabling unsupervised joint enhancement of graph structure and textual semantics via iterative pseudo-labeling. Furthermore, it integrates community signals into the Mamba architecture to construct the first annotation-free, graph-guided generative text classifier. Evaluated on multiple benchmark datasets, the approach significantly improves both structural cohesion and semantic consistency of detected communities. Remarkably, the Mamba classifier trained solely on community signals achieves accuracy comparable to fully supervised baselines, effectively bridging the gap between unsupervised graph representation learning and downstream text understanding.

0 citationsRead paper