Papers with Visual question answering
Being Negative but Constructively: Lessons Learnt from Creating Better Visual Question Answering Datasets (N18-1)
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| Challenge: | Visual question answering datasets are a form of (visual) Turing test that artificial intelligence should strive to achieve. |
| Approach: | They propose automatic procedures to remedy design deficiencies in visual question answering datasets . they propose to use a set of decoys to re-construct decoying answers for two popular Visual QA datasets. |
| Outcome: | The proposed procedures improve the performance of the proposed datasets. |
Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training (2022.findings-emnlp)
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| Challenge: | Existing approaches require substantial adaptation of pretrained language models for vision-language reasoning tasks. |
| Approach: | They propose to use natural language and network interpretation as an intermediate representation that glues pretrained models together. |
| Outcome: | The proposed framework outperforms the Flamingo model on VQAv2 and GQA by 8.5%. |
Visual Question Answering Dataset for Bilingual Image Understanding: A Study of Cross-Lingual Transfer Using Attention Maps (C18-1)
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| 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. |
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 . |
Generating Question Relevant Captions to Aid Visual Question Answering (P19-1)
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| Challenge: | Visual question answering and image captioning require a shared body of general knowledge connecting language and vision. |
| Approach: | They propose a method that exploits a shared body of general knowledge connecting language and vision by jointly generating captions. |
| Outcome: | The proposed approach obtains state-of-the-art performance on the VQA v2 challenge . it uses human annotated captions to generate question-relevant captions . |
Discovering the Unknown Knowns: Turning Implicit Knowledge in the Dataset into Explicit Training Examples for Visual Question Answering (2021.emnlp-main)
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| 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. |
Large Language Models Know What is Key Visual Entity: An LLM-assisted Multimodal Retrieval for VQA (2024.emnlp-main)
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| Challenge: | Existing visual language models struggle to capture longtail knowledge in the real world due to redundant visual information. |
| Approach: | They propose a method leveraging the reasoning capability of a large language model to identify key visual entities. |
| Outcome: | The proposed method outperforms other strong visual language model-based systems in two knowledge-intensive VQA benchmarks and performs comparably to models with 1-2 orders larger parameters. |
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. |
| 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 . |
Aligned Dual Channel Graph Convolutional Network for Visual Question Answering (2020.acl-main)
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| Challenge: | Existing graph-based methods focus only on relations between objects in an image and neglect the importance of syntactic dependency relations between words. |
| Approach: | They propose a dual channel graph convolutional network to capture relations between objects in an image and syntactic dependency relations between words in a question. |
| Outcome: | The proposed model achieves comparable performance with the state-of-the-art approaches. |
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. |
| Outcome: | The proposed framework improves on the multi-choice question-answering task using CLIP and BLIP models. |