Seeing and Reasoning: A Simple Deep Learning Approach to Visual Question Answering
Visual Question Answering (VQA) is a complex task that requires a deep understanding of both visual content and natural language questions. The challenge lies in enabling models to recognize and interpret visual elements and to reason through questions in a multi-step, compositional manner. We propo...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2025-04-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2024.9020079 |
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| author | Rufai Yusuf Zakari Jim Wilson Owusu Ke Qin Tao He Guangchun Luo |
| author_facet | Rufai Yusuf Zakari Jim Wilson Owusu Ke Qin Tao He Guangchun Luo |
| author_sort | Rufai Yusuf Zakari |
| collection | DOAJ |
| description | Visual Question Answering (VQA) is a complex task that requires a deep understanding of both visual content and natural language questions. The challenge lies in enabling models to recognize and interpret visual elements and to reason through questions in a multi-step, compositional manner. We propose a novel Transformer-based model that introduces specialized tokenization techniques to effectively capture intricate relationships between visual and textual features. The model employs an enhanced self-attention mechanism, enabling it to attend to multiple modalities simultaneously, while a co-attention unit dynamically guides focus to the most relevant image regions and question components. Additionally, a multi-step reasoning module supports iterative inference, allowing the model to excel at complex reasoning tasks. Extensive experiments on benchmark datasets demonstrate the model’s superior performance, with accuracies of 98.6% on CLEVR, 63.78% on GQA, and 68.67% on VQA v2.0. Ablation studies confirm the critical contribution of key components, such as the reasoning module and co-attention mechanism, to the model’s effectiveness. Qualitative analysis of the learned attention distributions further illustrates the model’s dynamic reasoning process, adapting to task complexity. Overall, our study advances the adaptation of Transformer architectures for VQA, enhancing both reasoning capabilities and model interpretability in visual reasoning tasks. |
| format | Article |
| id | doaj-art-5bbbc5e940b64e5f8a3949fbf6ce24ab |
| institution | OA Journals |
| issn | 2096-0654 2097-406X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-5bbbc5e940b64e5f8a3949fbf6ce24ab2025-08-20T02:04:30ZengTsinghua University PressBig Data Mining and Analytics2096-06542097-406X2025-04-018245847810.26599/BDMA.2024.9020079Seeing and Reasoning: A Simple Deep Learning Approach to Visual Question AnsweringRufai Yusuf Zakari0Jim Wilson Owusu1Ke Qin2Tao He3Guangchun Luo4School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaVisual Question Answering (VQA) is a complex task that requires a deep understanding of both visual content and natural language questions. The challenge lies in enabling models to recognize and interpret visual elements and to reason through questions in a multi-step, compositional manner. We propose a novel Transformer-based model that introduces specialized tokenization techniques to effectively capture intricate relationships between visual and textual features. The model employs an enhanced self-attention mechanism, enabling it to attend to multiple modalities simultaneously, while a co-attention unit dynamically guides focus to the most relevant image regions and question components. Additionally, a multi-step reasoning module supports iterative inference, allowing the model to excel at complex reasoning tasks. Extensive experiments on benchmark datasets demonstrate the model’s superior performance, with accuracies of 98.6% on CLEVR, 63.78% on GQA, and 68.67% on VQA v2.0. Ablation studies confirm the critical contribution of key components, such as the reasoning module and co-attention mechanism, to the model’s effectiveness. Qualitative analysis of the learned attention distributions further illustrates the model’s dynamic reasoning process, adapting to task complexity. Overall, our study advances the adaptation of Transformer architectures for VQA, enhancing both reasoning capabilities and model interpretability in visual reasoning tasks.https://www.sciopen.com/article/10.26599/BDMA.2024.9020079machine learningdeep learningvisual question answering (vqa)multi-step reasoningcomputer vision |
| spellingShingle | Rufai Yusuf Zakari Jim Wilson Owusu Ke Qin Tao He Guangchun Luo Seeing and Reasoning: A Simple Deep Learning Approach to Visual Question Answering Big Data Mining and Analytics machine learning deep learning visual question answering (vqa) multi-step reasoning computer vision |
| title | Seeing and Reasoning: A Simple Deep Learning Approach to Visual Question Answering |
| title_full | Seeing and Reasoning: A Simple Deep Learning Approach to Visual Question Answering |
| title_fullStr | Seeing and Reasoning: A Simple Deep Learning Approach to Visual Question Answering |
| title_full_unstemmed | Seeing and Reasoning: A Simple Deep Learning Approach to Visual Question Answering |
| title_short | Seeing and Reasoning: A Simple Deep Learning Approach to Visual Question Answering |
| title_sort | seeing and reasoning a simple deep learning approach to visual question answering |
| topic | machine learning deep learning visual question answering (vqa) multi-step reasoning computer vision |
| url | https://www.sciopen.com/article/10.26599/BDMA.2024.9020079 |
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