Jonas Pfeiffer, Gregor Geigle, Aishwarya Kamath, Jan-Martin Steitz, Stefan Roth, Ivan Vulić, Iryna Gurevych
| Challenge: | a lack of multilingual multimodal datasets has hindered multimodal vision and language modeling efforts. |
| Approach: | They propose a multilingual evaluation benchmark for the visual question answering task . they extend the established English GQA dataset to 7 typologically diverse languages . |
| Outcome: | The proposed methods outperform current state-of-the-art models in zero-shot cross-lingual settings, but the accuracy remains low across languages. |
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Delving Deeper into Cross-lingual Visual Question Answering (2023.findings-eacl)
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| Challenge: | Existing studies on cross-lingual VQA have reported poor zero-shot transfer performance of current multilingual multimodal Transformers . lack of multilingual resources has hindered development and evaluation of VQA methods beyond the English language . |
| Approach: | They analyze cross-lingual VQA across different question types of varying complexity . they show that simple modifications to the standard training setup can substantially reduce the transfer gap to monolingual English performance. |
| Outcome: | The proposed model significantly reduces the transfer gap to monolingual English performance . the proposed model also improves on question types and languages . |
MaXM: Towards Multilingual Visual Question Answering (2023.findings-emnlp)
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Soravit Changpinyo, Linting Xue, Michal Yarom, Ashish Thapliyal, Idan Szpektor, Julien Amelot, Xi Chen, Radu Soricut
| Challenge: | Visual Question Answering (VQA) has been studied in the English language, but in other languages it would require a considerable amount of resources. |
| Approach: | They propose scalable solutions to multilingual visual question answering using an English language framework and an annotation protocol. |
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Towards Multilingual spoken Visual Question Answering system using Cross-Attention (2025.coling-main)
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| Challenge: | Visual question answering (VQA) is a multi-modal translation challenge that requires the analysis of both images and questions simultaneously to generate appropriate responses. |
| Approach: | They propose a textless multilingual visual question answering dataset that incorporates speech-based questions in English, german, spanish and french. |
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M2QA: Multi-domain Multilingual Question Answering (2024.findings-emnlp)
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| Challenge: | Language varies along several axes, most importantly, language instance and domain . lack of evaluation datasets prevents transfer of NLP systems to non-dominant languages . |
| Approach: | They propose a multi-domain multilingual question answering benchmark to explore cross-lingual cross-domain performance of fine-tuned models and state-of-the-art LLMs. |
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Language Guided Visual Question Answering: Elevate Your Multimodal Language Model Using Knowledge-Enriched Prompts (2023.findings-emnlp)
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| Challenge: | Visual question answering (VQA) is a task that requires an understanding of both the image and the question to provide a natural language answer. |
| Approach: | They propose a multimodal framework that leverages language guidance to answer questions more accurately. |
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Multilingual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models (2021.naacl-main)
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| Challenge: | a new study examines zero-shot cross-lingual transfer of vision-language models . we study multilingual text-to-video search in non-English languages without annotations . |
| Approach: | They propose a Transformer-based model that learns contextual multilingual multimodal embeddings . they propose 'zero-shot cross-lingual transfer' to improve multilingual search . |
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Incorporating Probing Signals into Multimodal Machine Translation via Visual Question-Answering Pairs (2023.findings-emnlp)
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| Challenge: | Existing studies show that multimodal machine translation systems exhibit decreased sensitivity to visual information when text inputs are complete. |
| Approach: | They propose to generate parallel VQA style pairs from source text to foster more robust cross-modal interaction. |
| Outcome: | The proposed approach generates parallel VQA style pairs from the source text, fostering more robust cross-modal interaction. |
VQAGuider: Guiding Multimodal Large Language Models to Answer Complex Video Questions (2025.acl-long)
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| Challenge: | Multimodal large language models (MLLMs) can grasp the intention of a question and decomposing it to a series of visual recognition sub-tasks to find out the answer with the help of an agent. |
| Approach: | They propose a framework for multimodal large language models to grasp the intention of a question and decompose it into a series of visual recognition sub-tasks to find out the answer. |
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What Is Missing in Multilingual Visual Reasoning and How to Fix It (2025.findings-naacl)
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| Challenge: | NLP models today strive for supporting multiple languages and modalities, improving accessibility for diverse users. |
| Approach: | They propose a translation-test approach to tackle multilinguality, visual programming approach to break down complex reasoning, and a method that leverages image captioning to address multimodality. |
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NegVQA: Can Vision Language Models Understand Negation? (2025.findings-acl)
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| Challenge: | NegVQA is a visual question answering (VQA) benchmark consisting of 7,379 two-choice questions covering diverse negation scenarios and image-question distributions. |
| Approach: | They propose a visual question answering benchmark consisting of 7,379 two-choice questions covering diverse negation scenarios and image-question distributions. |
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