Connecting Vision and Language plays an essential role in Generative
Intelligence. For this reason, large research efforts have been devoted to
image captioning, i.e. describing images with syntactically and semantically
meaningful sentences. Starting from 2015 the task has generally been addressed
with pipelines composed of a visual encoder and a language model for text
generation. During these years, both components have evolved considerably
through the exploitation of object regions, attributes, the introduction of
multi-modal connections, fully-attentive approaches, and BERT-like early-fusion
strategies. However, regardless of the impressive results, research in image
captioning has not reached a conclusive answer yet. This work aims at providing
a comprehensive overview of image captioning approaches, from visual encoding
and text generation to training strategies, datasets, and evaluation metrics.
In this respect, we quantitatively compare many relevant state-of-the-art
approaches to identify the most impactful technical innovations in
architectures and training strategies. Moreover, many variants of the problem
and its open challenges are discussed. The final goal of this work is to serve
as a tool for understanding the existing literature and highlighting the future
directions for a research area where Computer Vision and Natural Language
Processing can find an optimal synergy.

本文综合研究图像描述中的视觉编码、文本生成、训练策略、数据集和评估指标等方面，量化比较多个相关的最前沿方法，以识别体系结构和训练策略中最具影响力的技术创新并探讨问题的许多变体和开放挑战，旨在为理解现有文献和强调计算机视觉和自然语言处理领域的未来方向提供工具。