Papers by Weixing Mai

2 papers
Ambiguity-aware Multi-level Incongruity Fusion Network for Multi-Modal Sarcasm Detection (2025.coling-main)

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Challenge: Existing methods for sarcasm detection focus on fusing text and image information to establish cross-modal correlations, overlooking the significance of original unimodal incongruity information.
Approach: They propose a multi-modal incongruity learning module to capture inconcluity information simultaneously at the text-level, image-level and cross-modal-level.
Outcome: The proposed model outperforms state-of-the-art methods on a publicly available dataset.
D2R: Dual-Branch Dynamic Routing Network for Multimodal Sentiment Detection (2024.emnlp-main)

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Challenge: Existing methods for multimodal sentiment detection use the same fixed framework to classify the sentiment polarity of image-text pairs.
Approach: They propose a multimodal dynamic interaction model that uses a fixed framework to classify the sentiment polarity of a given imagetext pair.
Outcome: The proposed model outperforms state-of-the-art models on three publicly available datasets.

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