| 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. |
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| Challenge: | Text-to-image retrieval is challenging because of cross-modal embeddings are bags of concepts, underrepresenting structured visual relationships. |
| Approach: | They propose a retrieval paradigm that embeds textual queries into the image modality via T2I generation and performs retrieval within the image mode to bypass weaknesses of cross-modal retrievers in recognizing subtle visual-spatial features. |
| Outcome: | The proposed retrieval paradigm outperforms previous approaches in visual-spatial retrieval benchmarks. |
SERVAL: Surprisingly Effective Zero-Shot Visual Document Retrieval Powered by Large Vision and Language Models (2025.emnlp-main)
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| Challenge: | Visual Document Retrieval (VDR) relies on text-to-image retrieval using specialized bi-encoders . et al., 2022, 2024, 2021, 2023, 2026, 2030, 2040, 2050, 2060) document retrieval bridges human or artificial agents to the most relevant information, authors say . |
| Approach: | They propose a zero-shot visual document retrieval method that uses bi-encoders . they propose 63.4% nDCG@5 for visual document capture and a reusable semantic proxy . |
| Outcome: | The proposed method surpasses the strongest specialised multi-vector visual document encoder on the ViDoRe-v2 benchmark and scales similarly on MIRACL-VISION with broader multilingual coverage. |
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. |
Seeing Through Words, Speaking Through Pixels: Deep Representational Alignment Between Vision and Language Models (2025.emnlp-main)
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| Challenge: | Recent studies show that deep vision-only and language-only models project inputs into a partially aligned representational space. |
| Approach: | They investigate whether a model's representational code is semantically shared . they find that alignment peaks in mid-to-late layers of both model types . |
| Outcome: | a forced-choice "Pick-a-Pic" task shows human preferences for image-caption matches are mirrored in embedding spaces across vision-language model pairs. |
Visually-Enhanced Phrase Understanding (2023.findings-acl)
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| Challenge: | Large-scale vision-language pre-training models generate high-quality textual representations, which often outperform models that are purely text-based, such as BERT. |
| Approach: | They propose to utilize both textual and visual encoders of multi-modal pre-trained models to enhance language understanding tasks by generating an image associated with a textual prompt. |
| Outcome: | The proposed method outperforms models that are purely text-based on visual and textual understanding tasks and significantly improves the entity clustering task. |
Improving Cross-Modal Alignment in Vision Language Navigation via Syntactic Information (2021.naacl-main)
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| Challenge: | Existing vision language navigation tasks require soft attention over words to locate instructions . a new approach uses syntax information to ground instructions with visual information . |
| Approach: | They propose a vision language navigation agent that utilizes syntax information to enhance alignment between the instruction and the current visual scenes. |
| Outcome: | The proposed agent outperforms the baseline model that does not use syntax information on the Room-to-Room dataset, especially in the unseen environment. |
Visuo-Linguistic Question Answering (VLQA) Challenge (2020.findings-emnlp)
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| Challenge: | Understanding images and text together is an important aspect of cognition and building advanced AI systems. |
| Approach: | They propose to derive joint inference about a given image-text modality and compile a question-answering corpus using an image and a reading passage. |
| Outcome: | The proposed method has better baseline performance but is still far behind human performance. |
VICTR: Visual Information Captured Text Representation for Text-to-Vision Multimodal Tasks (2020.coling-main)
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| Challenge: | Existing text-to-image generation models focus on generating high resolution images and neglect understanding text descriptions. |
| Approach: | They propose a visual contextual text representation which captures rich visual semantic information of objects from text input. |
| Outcome: | The proposed visual contextual text representation improves on the state-of-the-art models. |
Cross-modal Attention Congruence Regularization for Vision-Language Relation Alignment (2023.acl-long)
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| Challenge: | Despite recent progress towards scaling up multimodal vision-language models, these models struggle on compositional generalization benchmarks such as Winoground. |
| Approach: | They propose to use a cross-modal attention regularization loss to enforce relation alignment by capturing the semantic relation ‘in’ to match the visual attention from the mug to the grass. |
| Outcome: | The proposed approach improves Winoground Group score by 5.75 points . |
Cross-Lingual Representation Alignment Through Contrastive Image-Caption Tuning (2025.acl-short)
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| Challenge: | Multilingual alignment of sentence representations has mostly required bitexts to bridge the gap between languages. |
| Approach: | They propose to use image captions to implicitly align text representations between languages to make them usable for cross-lingual Natural Language Understanding (NLU) and bitext retrieval. |
| Outcome: | The proposed approach is usable for cross-lingual Natural Language Understanding (NLU) and bitext retrieval. |