AraVQA: Building a New Arabic Factoid Visual Question Answering Dataset from Wikipedia (2026.acl-long)
Copied to clipboard
| Challenge: | Existing Arabic VQA datasets focus on culturally-specific and dialect-aware domains. |
| Approach: | They propose a pipeline that leverages Wikipedia template tags to extract relevant information for each image and utilize it to generate a new visual question answering dataset. |
| Outcome: | The proposed pipeline can enhance existing VLMs on Arabic VQA tasks by leveraging Wikipedia template tags. |
Similar Papers
HaVQA: A Dataset for Visual Question Answering and Multimodal Research in Hausa Language (2023.findings-acl)
Copied to clipboard
Shantipriya Parida, Idris Abdulmumin, Shamsuddeen Hassan Muhammad, Aneesh Bose, Guneet Singh Kohli, Ibrahim Said Ahmad, Ketan Kotwal, Sayan Deb Sarkar, Ondřej Bojar, Habeebah Kakudi
| Challenge: | Existing models for visual question answering are limited to the English language. |
| Approach: | They present a multimodal dataset for visual question answering tasks in the Hausa language. |
| Outcome: | The proposed dataset provides 12,044 gold standard English-Hausa parallel sentences that are semantically identical to the corresponding visual information. |
A Corpus for Visual Question Answering Annotated with Frame Semantic Information (2020.lrec-1)
Copied to clipboard
| Challenge: | Visual Question Answering (VQA) is a computer vision problem. |
| Approach: | They propose to annotate a visual question answering dataset with verb semantics to help the model understand verbs. |
| Outcome: | The proposed system is built on the imSitu dataset annotated with verb semantic information. |
All You May Need for VQA are Image Captions (2022.naacl-main)
Copied to clipboard
| Challenge: | Visual Question Answering (VQA) has benefited from increasingly sophisticated models, but has not enjoyed the same level of engagement in terms of data creation. |
| Approach: | They propose a method that automatically derives VQA examples at volume by leveraging existing image-caption annotations combined with neural models for textual question generation. |
| Outcome: | The proposed method improves state-of-the-art zero-shot accuracy by double digits and achieves robustness that lacks in the same model trained on human-annotated VQA data. |
FVQA 2.0: Introducing Adversarial Samples into Fact-based Visual Question Answering (2023.findings-eacl)
Copied to clipboard
| Challenge: | Fact-based Visual Question Answering (FVQA) is a visual question answering task that requires information retrieval using common sense knowledge graphs to answer. |
| Approach: | They propose a new test question with adversarial variants to address this imbalance by using a KB-VQA dataset that is small and contains only one answer per question. |
| Outcome: | The proposed version reduces the vulnerability of the original FVQA dataset without human annotations. |
‘Just because you are right, doesn’t mean I am wrong’: Overcoming a bottleneck in development and evaluation of Open-Ended VQA tasks (2021.eacl-main)
Copied to clipboard
Man Luo, Shailaja Keyur Sampat, Riley Tallman, Yankai Zeng, Manuha Vancha, Akarshan Sajja, Chitta Baral
| Challenge: | Existing visual question answering datasets assume only one ground truth answer for each question. |
| Approach: | They propose alternative answer sets (AAS) of ground-truth answers to address this limitation . they modify top VQA solvers to support multiple plausible answers for a question . |
| Outcome: | The proposed approach improves on the GQA dataset and shows that it is more efficient than previous approaches. |
CommVQA: Situating Visual Question Answering in Communicative Contexts (2024.emnlp-main)
Copied to clipboard
| Challenge: | Current visual question answering models are trained on image-question pairs in isolation, but the questions people ask are dependent on their informational needs and prior knowledge about the image content. |
| Approach: | They propose a visual question-answer-as-question dataset that contains 1000 images and 8,949 question-announcer pairs to evaluate how situating images within naturalistic contexts shapes visual questions. |
| Outcome: | The proposed dataset contains 1000 images and 8,949 question-answer pairs. |
MaXM: Towards Multilingual Visual Question Answering (2023.findings-emnlp)
Copied to clipboard
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. |
Video Question Answering: Datasets, Algorithms and Challenges (2022.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in video question answering have led to a surge in popularity . despite the popularity, VideoQA remains one of the greatest challenges . |
| Approach: | They categorize the video question-answer datasets into normal VideoQA, multi-modal VideoQA and knowledge-based VideoQA according to the modalities invoked in the question-announcement pairs. |
| Outcome: | The proposed methods are mainly designed for Factoid QA and inference VideoQA . the proposed methods have been compared with other methods and are robust and interpretable. |
Filling the Image Information Gap for VQA: Prompting Large Language Models to Proactively Ask Questions (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) demonstrate impressive reasoning ability and the maintenance of world knowledge in natural language tasks. |
| Approach: | They propose a framework that enables LLMs to ask relevant questions to uncover more details in the image, along with filters for refining the generated information. |
| Outcome: | The proposed framework boosts the performance of baseline methods by 2.15% on OK-VQA and achieves consistent improvements across different LLMs. |
Discovering the Unknown Knowns: Turning Implicit Knowledge in the Dataset into Explicit Training Examples for Visual Question Answering (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods address this issue by introducing an auxiliary task such as visual grounding, cycle consistency, or debiasing. |
| Approach: | They propose a data augmentation pipeline to turn “known” knowledge into training examples for VQA. |
| Outcome: | The proposed model can handle multi-modal information and is based on human-annotated examples. |