Automated image caption generation is a critical area of research that
enhances accessibility and understanding of visual content for diverse
audiences. In this study, we propose the FaceAtt model, a novel approach to
attribute-focused image captioning that emphasizes the accurate depiction of
facial attributes within images. FaceAtt automatically detects and describes a
wide range of attributes, including emotions, expressions, pointed noses, fair
skin tones, hair textures, attractiveness, and approximate age ranges.
Leveraging deep learning techniques, we explore the impact of different image
feature extraction methods on caption quality and evaluate our model's
performance using metrics such as BLEU and METEOR. Our FaceAtt model leverages
annotated attributes of portraits as supplementary prior knowledge for our
portrait images before captioning. This innovative addition yields a subtle yet
discernible enhancement in the resulting scores, exemplifying the potency of
incorporating additional attribute vectors during training. Furthermore, our
research contributes to the broader discourse on ethical considerations in
automated captioning. This study sets the stage for future research in refining
attribute-focused captioning techniques, with a focus on enhancing linguistic
coherence, addressing biases, and accommodating diverse user needs.

自动图像标题生成是一个关键的研究领域，通过 FaceAtt 模型提出了一种强调准确描绘图像中面部属性的新方法，自动检测和描述多种属性，并使用深度学习技术评估了模型的性能和指标。此外，研究还涉及到自动标题生成中的伦理考虑，为未来改进属性关注的标题生成技术、提高语言连贯性、解决偏见问题和满足多样化用户需求提供了基础。