Improving Cross-modal Alignment for Text-Guided Image Inpainting (2023.eacl-main)
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| Challenge: | Existing methods allocate most of computation to visual encoding, while light computation on modeling modality interactions. |
| Approach: | They propose a novel model for text-guided image inpainting by improving cross-modal alignment knowledge by using a vision-language encoder and an image generator. |
| Outcome: | The proposed model achieves state-of-the-art performance compared with other strong competitors on two vision-language datasets. |
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ITA: Image-Text Alignments for Multi-Modal Named Entity Recognition (2022.naacl-main)
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| Challenge: | Recent work on Multi-modal Named Entity Recognition (MNER) relies on image information to model interactions between image and text representations. |
| Approach: | They propose to align image features into the textual space to better utilize attention mechanisms . they use regional object tags, captions and optical characters as visual contexts . |
| Outcome: | The proposed model can achieve state-of-the-art accuracy on multi-modal Named Entity Recognition datasets even without image information. |
Semantics-enhanced Cross-modal Masked Image Modeling for Vision-Language Pre-training (2024.lrec-main)
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Haowei Liu, Yaya Shi, Haiyang Xu, Chunfeng Yuan, Qinghao Ye, Chenliang Li, Ming Yan, Ji Zhang, Fei Huang, Bing Li, Weiming Hu
| Challenge: | Existing methods for vision-language pre-training lack high-level semantics and text is not sufficiently involved in masked modeling. |
| Approach: | They propose a semantics-enhanced cross-modal MIM framework for vision-language representation learning that harvests high-level semantics from global image features via self-supervised agreement learning and transfers them to local patch encodings by sharing the encode space. |
| Outcome: | The proposed model achieves state-of-the-art or competitive performance on multiple vision-language tasks. |
Probing Cross-modal Semantics Alignment Capability from the Textual Perspective (2022.findings-emnlp)
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| Challenge: | In recent years, vision and language pre-training (VLP) models have advanced the state-of-the-art results in a variety of cross-modal downstream tasks. |
| Approach: | They propose a new probing method that is based on image captioning to first empirically study the cross-modal semantics alignment of VLP models. |
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CmEAA: Cross-modal Enhancement and Alignment Adapter for Radiology Report Generation (2025.coling-main)
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| Challenge: | Existing methods for automatic radiology report generation suffer from data bias. |
| Approach: | They propose a method that connects a vision encoder with a frozen large language model by using a cross-modal enhancement and alignment adapter. |
| Outcome: | The proposed model outperforms existing state-of-the-art methods on IU X-Ray and MIMIC-CXR datasets. |
Conditioned Masked Language and Image Modeling for Image-Text Dense Retrieval (2022.findings-emnlp)
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| Challenge: | Large-scale two-stream pre-trained models like CLIP have achieved tremendous success in image-text retrieval. |
| Approach: | They propose a cross-modal framework for image-text retrieval using two-stream pre-trained models . they embed images and texts into instance representations with two separate encoders . experimental results on MSCOCO and Flickr30k reveal the effectiveness of their framework . |
| Outcome: | The proposed framework improves image-text retrieval performance on two popular cross-modal retrieval benchmarks. |
PV2TEA: Patching Visual Modality to Textual-Established Information Extraction (2023.findings-acl)
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| Challenge: | Empirical results show up to 11.74% absolute (20.97% relative) increase over unimodal baselines. |
| Approach: | They propose to patch the visual modality to the textual-established attribute in- formation extractor. |
| Outcome: | Empirical results show up to 11.74% absolute (29.9% relative) increase over unimodal baselines. |
MCAD: Multi-teacher Cross-modal Alignment Distillation for efficient image-text retrieval (2024.findings-naacl)
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| Challenge: | Large-scale visual-language pretraining models have shown remarkable capabilities in understanding both vision and language. |
| Approach: | They propose a multi-teacher cross-modality alignment distillation technique to integrate the advantages of single-stream and dual-stream models. |
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Cross-Modal Attribute Insertions for Assessing the Robustness of Vision-and-Language Learning (2023.acl-long)
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| Challenge: | Existing approaches to model multimodal data do not leverage cross-modal information . augmenting input text using cross-module attribute insertions results in poor performance . |
| Approach: | They propose a multimodal deep learning approach that adds visual attributes to inputs to enhance model robustness. |
| Outcome: | The proposed approach is modular, controllable, and task-agnostic. |
CLASP: Cross-modal Alignment Using Pre-trained Unimodal Models (2024.findings-acl)
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| Challenge: | Recent advances in speech-text pretraining rely on parallel speech- text data . however, data accessibility is a challenge due to the limited data available. |
| Approach: | They propose a framework for jointly performing speech and text processing without parallel corpora during pre-training but only downstream. |
| Outcome: | The proposed framework extracts distinct representations for speech and text, aligning them effectively in a newly defined space using a multi-level contrastive learning mechanism. |
Beyond Cross-Modal Alignment: Measuring and Leveraging Modality Gap in Vision-Language Models (2026.findings-acl)
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| Challenge: | a recent study shows that vision-language models have modality gaps that persist even in well-aligned models. |
| Approach: | They propose a modality-dominance score to measure and leverage modality gaps . they propose automatic interpretability metrics to evaluate these features in a scalable manner . |
| Outcome: | The proposed framework allows for training-free probing and editing methods for understanding model perception across genders and generating adversarial examples. |