📄 中文摘要
该研究提出了一种新的卷积神经网络架构，称为ABC-CNN，旨在提升视觉问答（VQA）任务的性能。ABC-CNN结合了卷积神经网络和注意力机制，通过对图像和问题的有效特征提取与融合，增强了模型对复杂视觉信息的理解能力。实验结果表明，ABC-CNN在多个VQA基准数据集上均取得了优异的表现，展示了其在处理视觉信息与语言信息结合方面的潜力。该模型的设计思路为未来的视觉问答研究提供了新的方向。
ABC-CNN：一种基于注意力机制的卷积神经网络用于视觉问答
📄 English Summary
ABC-CNN: An Attention Based Convolutional Neural Network for Visual QuestionAnswering
The study presents a novel convolutional neural network architecture called ABC-CNN, aimed at enhancing performance in visual question answering (VQA) tasks. ABC-CNN integrates convolutional neural networks with attention mechanisms, effectively extracting and fusing features from images and questions to improve the model's understanding of complex visual information. Experimental results demonstrate that ABC-CNN achieves outstanding performance across multiple VQA benchmark datasets, showcasing its potential in handling the integration of visual and linguistic information. The design approach of this model offers new directions for future research in visual question answering.