Papers with cross-modal representation learning

2 papers
Multi-Scale Spectral Selection and Entropy-Guided Uncertainty Fusion for Multimodal Rumor Detection (2026.findings-acl)

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Challenge: Existing methods for multimodal content detection fail to capture cross-modal semantic inconsistencies and ignore inherent noise in multimodal features.
Approach: They propose a multimodal rumor detection method based on a frequency domain spectral selection method and entropy-guided uncertainty fusion method to capture cross-modal semantic inconsistencies.
Outcome: The proposed method outperforms state-of-the-art methods in multimodal rumor detection . it shows stronger detection capability and robustness on multiple datasets .
Exploring Compositional Image Retrieval with Hybrid Compositional Learning and Heuristic Negative Mining (2022.findings-emnlp)

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Challenge: Existing CIR models are pre-trained on uni-modal data, resulting in unimodal data.
Approach: They propose a CIR model HyCoLe-HNM with CLIP as the backbone . they use a gated fusion mechanism from a question answering model to perform compositional learning .
Outcome: The proposed model achieves state-of-the-art performance on three CIR datasets . it borrows a gated fusion mechanism from a question answering model to perform compositional fusion .

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