对比注意力模型与RNN，发现前者生成更准确的图文描述
Beyond RNNs: Benchmarking Attention-Based Image Captioning Models
- 用注意力机制对齐图像特征与文本生成
- 在MS-COCO数据集上优于RNN模型，提升语义丰富度
- 适合关注视觉语言对齐与生成质量的研究者
图像字幕生成是计算机视觉与自然语言处理的交叉挑战，需为图像生成有意义的文本描述。传统方法依赖循环神经网络（RNN），但注意力机制的进展显著提升了性能。本研究在MS-COCO数据集上对比了基于注意力的图像字幕模型与RNN方法。评估了Bahdanau注意力在提升图像特征与生成字幕间对齐效果的作用。采用BLEU、METEOR、GLEU和WER等自然语言评价指标进行测试。结果表明，注意力模型在生成更准确、语义更丰富的字幕方面优于RNN，且与人工评价更具一致性。该工作揭示了注意力机制在图像字幕中的影响，并指出了未来改进方向。
原文摘要 · Abstract (English)
Image captioning is a challenging task at the intersection of computer vision and natural language processing, requiring models to generate meaningful textual descriptions of images. Traditional approaches rely on recurrent neural networks (RNNs), but recent advancements in attention mechanisms have demonstrated significant improvements. This study benchmarks the performance of attention-based image captioning models against RNN-based approaches using the MS-COCO dataset. We evaluate the effectiveness of Bahdanau attention in enhancing the alignment between image features and generated captions. The models are assessed using natural language processing metrics such as BLEU, METEOR, GLEU, and WER. Our results show that attention-based models outperform RNNs in generating more accurate and semantically rich captions, with better alignment to human evaluation. This work provides insights into the impact of attention mechanisms in image captioning and highlights areas for future improvements.
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