Papers with MMSD
T-MAD: Target-driven Multimodal Alignment for Stance Detection (2025.emnlp-main)
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| Challenge: | Existing methods for Multimodal Stance Detection struggle with generalizing to unseen targets and handling modality inconsistencies. |
| Approach: | They propose a multimodal stability detection model which captures target-specific relationships and balances modality contributions by iterative reasoning. |
| Outcome: | Experiments on the MMSD and MultiClimate datasets show that the proposed model outperforms state-of-the-art models with optimal results achieved using RoBERTa, ViT, and an iterative depth of 5. |
MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System (2023.findings-acl)
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| Challenge: | Existing benchmarks for multi-modal sarcasm detection have some shortcomings . a new framework can leverage multi-grained cues from multiple perspectives for multimodal detection . |
| Approach: | They propose a correction dataset that removes spurious cues and re-annotates the unreasonable samples. |
| Outcome: | The proposed framework outperforms the existing benchmarks in multi-modal sarcasm detection. |
Multi-modal Semantic Understanding with Contrastive Cross-modal Feature Alignment (2024.lrec-main)
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| Challenge: | Current work on multi-modal semantic understanding primarily exploits a dual-encoder structure to separate image and text, but fails to learn cross-modal feature alignment. |
| Approach: | They propose a CLIP-guided contrastive-learning-based architecture to perform multi-modal feature alignment by projecting features from different modalities into a unified deep space. |
| Outcome: | The proposed model outperforms baseline models on sarcasm detection and sentiment analysis tasks and is simple to implement without using task-specific external knowledge. |
MIND Your Reasoning: A Meta-Cognitive Intuitive-Reflective Network for Dual-Reasoning in Multimodal Stance Detection (2026.acl-long)
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| Challenge: | Existing methods operate by learning to fuse modalities, leading to frequent misjudgments. |
| Approach: | They propose a paradigm shift from *learning to fuse* to *learning the reason's process' inspired by the dual-process theory of human cognition, MIND operationalizes a self-improving loop. |
| Outcome: | The proposed model significantly outperforms baseline models and exhibits strong generalization. |