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. |
| Outcome: | The proposed framework reduces human annotation efforts and creates a test-only VQA benchmark in 7 languages. |
Similar Papers
xGQA: Cross-Lingual Visual Question Answering (2022.findings-acl)
Copied to clipboard
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. |
Towards Multilingual spoken Visual Question Answering system using Cross-Attention (2025.coling-main)
Copied to clipboard
| 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. |
| Outcome: | The proposed framework is superior to existing frameworks for speech-based VQA . the proposed framework can generate better results for image, text and audio representations . |
Delving Deeper into Cross-lingual Visual Question Answering (2023.findings-eacl)
Copied to clipboard
| 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 . |
MTVQA: Benchmarking Multilingual Text-Centric Visual Question Answering (2025.findings-acl)
Copied to clipboard
Jingqun Tang, Qi Liu, Yongjie Ye, Jinghui Lu, Shu Wei, An-Lan Wang, Chunhui Lin, Hao Feng, Zhen Zhao, Yanjie Wang, Yuliang Liu, Hao Liu, Xiang Bai, Can Huang
| Challenge: | Text-Centric Visual Question Answering (TEC-VQA) is a text-centric visual task understanding tool. |
| Approach: | They introduce a benchmark that features human expert annotations across 9 languages . they prioritize the text in question-answer pairs while disregarding visual text in images . |
| Outcome: | The proposed benchmarks prioritize the text in question-answer pairs while disregarding visual text in images. |
VQAGuider: Guiding Multimodal Large Language Models to Answer Complex Video Questions (2025.acl-long)
Copied to clipboard
| 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. |
| Outcome: | The proposed framework improves the accuracy of complex video-related questions by 29.6% and 17.2% on CVQA and the existing VQA datasets. |
Open-Ended Visual Question Answering by Multi-Modal Domain Adaptation (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to visual question answering (VQA) are not suitable for real-world applications. |
| Approach: | They propose a supervised multi-modal domain adaptation method for visual question answering in images that exploits supervised domain adaptation. |
| Outcome: | The proposed method outperforms state-of-the-art methods on the benchmark VQA 2.0 and VizWiz datasets. |
A Unified Framework for Multilingual and Code-Mixed Visual Question Answering (2020.aacl-main)
Copied to clipboard
| Challenge: | Existing techniques for visual question answering focus on English questions, but many applications require a multilingual module. |
| Approach: | They propose a deep learning framework for multilingual and code- mixed visual question answering . they create Hindi and Code-mixed VQA datasets by exploiting linguistic properties of these languages . |
| Outcome: | The proposed model is capable of predicting answers from the questions in Hindi, English or Code- mixed (Hindi-English) languages. |
Language Guided Visual Question Answering: Elevate Your Multimodal Language Model Using Knowledge-Enriched Prompts (2023.findings-emnlp)
Copied to clipboard
| 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. |
| Outcome: | The proposed framework improves on the multi-choice question-answering task using CLIP and BLIP models. |
Visual Question Answering Dataset for Bilingual Image Understanding: A Study of Cross-Lingual Transfer Using Attention Maps (C18-1)
Copied to clipboard
| Challenge: | Existing literature on visual question answering (VQA) focuses on English, but there are no datasets for other languages. |
| Approach: | They propose a cross-lingual method to make use of English annotation to improve Japanese VQA . they use attention maps generated from English questions to improve the task . |
| Outcome: | The proposed method performs better than using a monolingual corpus in Japanese than using monolingual ones. |
TVQACML: Benchmarking Text-Centric Visual Question Answering in Multilingual Chinese Minority Languages (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing TEC-VQA benchmarks focus on high-resource languages like English and Chinese . existing benchmarks have a "visual-textual misalignment" problem resulting in unreliable evaluation results . |
| Approach: | They propose a benchmark that expands multilingual QA pairs in non-text-centric datasets through translation to eight languages, including Standard Chinese, Korean, and six minority languages. |
| Outcome: | The proposed benchmarks are contamination-free and more challenging . they include eight languages including Chinese, Korean, and six minority languages . |