Papers with cross-modal representations
Progressive Visual Refinement for Multi-modal Summarization (2026.eacl-short)
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| Challenge: | Multi-modal summarization (MMS) is a critical research area driven by the proliferation of multimedia content. |
| Approach: | They propose a patch-refined visual information network to exploit multimodal information . they propose combining visual information with textual information to generate concise summaries . |
| Outcome: | Extensive experiments on two public MMS datasets show the superiority of the proposed model. |
RespiraMFM: A Multimodal Foundation Model with Contrastive Audio-Language Alignment for Respiratory Disease Identification (2026.acl-long)
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| Challenge: | Existing models for respiratory diseases rely on audio inputs, but they lack generalizability and diagnostic precision. |
| Approach: | They propose a multimodal foundation model that integrates respiratory sounds with medical history and symptoms to enhance diagnostic accuracy and disease detection capabilities. |
| Outcome: | The proposed model improves AUROC and zero-shot tasks across five respiratory diseases using real-world datasets. |
Finding and Editing Multi-Modal Neurons in Pre-Trained Transformers (2024.findings-acl)
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| Challenge: | Existing methods to identify key neurons for interpretability of multi-modal large language models are unclear. |
| Approach: | They propose a method to identify key neurons for interpretability by multi-modal large language models. |
| Outcome: | The proposed method improves conventional works upon efficiency and applied range by removing needs of costly gradient computation. |
Tackling Ambiguity with Images: Improved Multimodal Machine Translation and Contrastive Evaluation (2023.acl-long)
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| Challenge: | Recent work in multimodal machine translation (MT) has shown that ambiguity can be resolved using accompanying context such as images. |
| Approach: | They propose a multimodal machine translation approach based on a strong text-only MT model and a novel guided self-attention mechanism to train it. |
| Outcome: | The proposed model outperforms existing models on EnglishFrench, EnglishGerman and EnglishCzech benchmarks and is freely available. |
Probing Multi-modal Machine Translation with Pre-trained Language Model (2021.findings-acl)
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| Challenge: | Multi-modal machine translation (MMT) aimed at using images to help disambiguate the target during translation but recent studies showed that visual features are either negligible or incremental. |
| Approach: | They propose to incorporate a visual language model on the source side to improve multi-modal translation quality significantly. |
| Outcome: | The proposed model improves the translation quality significantly on the multi-modal dataset. |
Weakly Supervised Vision-and-Language Pre-training with Relative Representations (2023.acl-long)
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| Challenge: | Weakly supervised vision-and-language pre-training (WVLP) uses only local descriptions of images as cross-modal anchors to construct weakly-aligned image-text pairs for pre- training. |
| Approach: | They propose to take a small number of aligned image-text pairs as anchors and represent each unaligned image and text by its similarities to these anchors. |
| Outcome: | The proposed model reduces the cost of pre-training while maintaining decent performance on downstream tasks. |
Vision-and-Language or Vision-for-Language? On Cross-Modal Influence in Multimodal Transformers (2021.emnlp-main)
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| Challenge: | Pretrained vision-and-language BERTs aim to learn representations that combine information from both modalities. |
| Approach: | They propose a diagnostic method based on cross-modal input ablation to assess the extent to which pretrained models integrate cross-module information. |
| Outcome: | The proposed method evaluates the model's performance on the other modality based on inputs from one or both modality. |
LightVLP: A Lightweight Vision-Language Pre-training via Gated Interactive Masked AutoEncoders (2024.lrec-main)
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| Challenge: | Existing vision-language pre-training models use multi-modal encoders to encode image and text, causing noisy training corpora. |
| Approach: | They propose a vision-language pre-training framework with two autoencoders for efficient training . they propose masked tokens and a gated interaction mechanism to cope with noise . |
| Outcome: | The proposed model achieves 2.2% R@1 gains on COCO Text Retrieval and 1.1% on refCOCO+ on six datasets. |
See Detail Say Clear: Towards Brain CT Report Generation via Pathological Clue-driven Representation Learning (2024.findings-emnlp)
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| Challenge: | Brain CT report generation is important to aid physicians in diagnosing cranial diseases. |
| Approach: | They propose a Pathological Clue-driven Representation Learning model to build cross-modal representations based on pathological clues and adapt them for text generation. |
| Outcome: | The proposed method outperforms previous methods and achieves SoTA performance. |
Visual-Linguistic Dependency Encoding for Image-Text Retrieval (2024.lrec-main)
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| Challenge: | Existing approaches to image-text retrieval ignore semantic discrepancies caused by syntactic structure in natural language expressions and relationships among visual entities. |
| Approach: | They propose a visual-linguistic dependency encoder framework which explicitly models the dependency information among textual words and interaction patterns between image regions. |
| Outcome: | The proposed framework outperforms existing methods on a vision-linguistic compositional structure reasoning dataset. |
Beyond Static Alignment: Adaptive Arbitration for Semantic Incongruence in Semi-Supervised Multimodal Sentiment Analysis (2026.acl-long)
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| Challenge: | Existing methods for semantic incongruence in sentiment analysis are limited by label-limited settings. |
| Approach: | They propose a framework for semi-supervised multimodal sentiment analysis that emphasizes stable cross-modal representations and reliable supervision. |
| Outcome: | The proposed framework outperforms state-of-the-art methods under label-limited settings. |