  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.
