用CNN与Transformer自动为图像生成描述，提升准确性和自然度。
Automated Image Captioning with CNNs and Transformers
- 结合CNN提取图像特征，Transformer生成自然语言描述。
- 在图文数据集上训练，使用BLEU、METEOR等指标评估效果。
- 优化注意力机制与超参数，提升生成质量，适合图像理解研究者。
本项目旨在构建一个自动化图像字幕系统，通过融合计算机视觉与自然语言处理技术，为输入图像生成自然语言描述。采用从CNN-RNN到更先进的基于Transformer的技术方案。在带有描述性字幕的图像数据集上进行训练，使用BLEU、METEOR和CIDEr等标准指标评估模型性能。实验还涉及先进注意力机制的探索、不同架构对比以及超参数优化，以提升字幕生成准确率与系统整体有效性。
原文摘要 · Abstract (English)
This project aims to create an automated image captioning system that generates natural language descriptions for input images by integrating techniques from computer vision and natural language processing. We employ various different techniques, ranging from CNN-RNN to the more advanced transformer-based techniques. Training is carried out on image datasets paired with descriptive captions, and model performance will be evaluated using established metrics such as BLEU, METEOR, and CIDEr. The project will also involve experimentation with advanced attention mechanisms, comparisons of different architectural choices, and hyperparameter optimization to refine captioning accuracy and overall system effectiveness.
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