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Leiquan Wang

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2 papers

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AAAI Conference 2025 Conference Paper

Multiple Feature Refining Network for Visual Emotion Distribution Learning

Qinfu Xu
Shaozu Yuan
Yiwei Wei
Jie Wu
Leiquan Wang
Chunlei Wu

The significance of visual emotion distribution learning (VEDL) has surged, particularly with the growing inclination to convey emotions through images. The key of VEDL lies in capturing both low- and high-level features within the same visual content, thus promoting the model for salient and subtle emotion awareness. To learn the distribution of emotions involved in images, most previous works learn coarse semantic knowledge with unbiased filtering. Consequently, they focus on the entire scene and suffer from the redundancy of semantic-irrelevant information, which diminishes the affective coherence, impeding the comprehension of emotional attributes within the treated features. In light of this, we reanalyze from the perspective of information filtering and propose a novel method called Multiple Feature Refining Network (MFRN). To minimize low-level feature redundancy, we design a wavelet-based separated frequency modeling, named Spectral Mixer, to learn invariant representations and enhance emotion saliency in low-level image features. At the higher semantic level, we design a Semantic Graph Prompt Learning for emotional semantic filtering, ensuring the purity of emotional information and providing the model with richer content semantics. Experiments conducted on three commonly used datasets have demonstrated the superiority of our MFRN model over cutting-edge methods.

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AAAI Conference 2025 Conference Paper

Towards Multimodal Sentiment Analysis via Hierarchical Correlation Modeling with Semantic Distribution Constraints

Qinfu Xu
Yiwei Wei
Chunlei Wu
Leiquan Wang
Shaozu Yuan
Jie Wu
Jing Lu
Hengyang Zhou

Sentiment analysis is rapidly advancing by utilizing various data modalities (e.g., text, video, and audio). However, most existing techniques only learn the atomic-level features that reflect strong correlations, while ignoring more complex compositions in multimodal data. Moreover, they also neglected the incongruity in semantic distribution among modalities. In light of this, we introduce a novel Hierarchical Correlation Modeling Network (HCMNet), which enhances the multimodal sentiment analysis by exploring both the atomic-level correlations based on dynamic attention reasoning and the composition-level correlations through topological graph reasoning. In addition, we also alleviate the impact of distributional inconsistencies between modalities from both atomic-level and composition-level perspectives. Specifically, we first design an atomic-level contrastive loss that constrains the semantic distribution across modalities to mitigate the atomic-level inconsistency. Then, we design a graph optimal transport module that integrates transport flows with different graphs to constrain the composition-level semantic distribution, thus reducing the inconsistency of compositional nodes. Experiments on three public benchmark datasets have demonstrated the superiority of the proposed model over the state-of-the-art methods.

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