This research explores the realm of neural image captioning using deep learning models. The study investigates the performance of different neural architecture configurations, focusing on the inject architecture, and proposes a novel quality metric for evaluating caption generation. Through extensive experimentation and analysis, this work sheds light on the challenges and opportunities in image captioning, providing insights into model behavior and overfitting. The results reveal that while the merge models exhibit a larger vocabulary and higher ROUGE scores, the inject architecture generates relevant and concise image captions. The study also highlights the importance of refining training data and optimizing hyperparameters for improved model performance. This research contributes to the growing body of knowledge in neural image captioning and encourages further exploration in the field, emphasizing the democratization of artificial intelligence.

翻译：本研究探索了利用深度学习模型进行神经图像字幕生成的领域。通过重点研究"注入"架构下不同神经网络配置的性能表现，提出了一种用于评估字幕生成质量的新颖度量指标。通过广泛的实验与分析，本文揭示了图像字幕生成过程中的挑战与机遇，提供了对模型行为及过拟合现象的深入理解。研究结果表明：虽然"融合"模型拥有更大的词汇量和更高的ROUGE评分，但"注入"架构能够生成相关且简洁的图像字幕。研究同时强调了优化训练数据和超参数对提升模型性能的重要性。本研究成果丰富了神经图像字幕生成领域的知识体系，为推动人工智能民主化进程提供了新思路，并鼓励该领域的进一步探索。