📄 中文摘要
该研究提出了一种简单的基线方法用于视觉问答（VQA）任务。通过结合图像特征和问题文本，模型能够有效地生成答案。研究中使用了卷积神经网络（CNN）提取图像特征，并利用循环神经网络（RNN）处理文本问题。这种方法在多个数据集上进行了评估，结果显示其在准确性和效率方面具有良好的表现。该基线方法为后续的复杂模型提供了参考，展示了在视觉问答领域中简单模型的潜力。
视觉问答的简单基线
出处: Simple Baseline for Visual Question Answering
发布: 2026年2月22日
📄 English Summary
Simple Baseline for Visual Question Answering
A simple baseline method for Visual Question Answering (VQA) tasks is proposed. By combining image features and textual questions, the model effectively generates answers. Convolutional Neural Networks (CNNs) are used to extract image features, while Recurrent Neural Networks (RNNs) handle the textual questions. Evaluations on multiple datasets demonstrate good performance in terms of accuracy and efficiency. This baseline method serves as a reference for more complex models, showcasing the potential of simple models in the field of visual question answering.