The goal of fine-grained image description generation techniques is to learn detailed information from images and simulate human-like descriptions that provide coherent and comprehensive textual details about the image content. Currently, most of these methods face two main challenges: description repetition and omission. Moreover, the existing evaluation metrics cannot clearly reflect the performance of models on these two issues. To address these challenges, we propose an innovative Fine-grained Image Description Generation model based on Joint Objectives. Furthermore, we introduce new object-based evaluation metrics to more intuitively assess the model's performance in handling description repetition and omission. This novel approach combines visual features at both the image level and object level to maximize their advantages and incorporates an object penalty mechanism to reduce description repetition. Experimental results demonstrate that our proposed method significantly improves the CIDEr evaluation metric, indicating its excellent performance in addressing description repetition and omission issues.

提出了基于联合目标的创新细粒度图像描述生成模型，结合图像级别和对象级别的视觉特征，通过对象惩罚机制减少描述重复，并引入新的基于对象的评估度量来直观地评估模型在处理描述重复和遗漏问题上的性能。实验结果表明，该方法显著改善了CIDEr评估度量，并显示出在解决描述重复和遗漏问题方面的出色性能。

基于联合目标的细粒度图像描述生成方法