真实世界图像描述与场景识别的综合分析
A Comprehensive Analysis of Real-World Image Captioning and Scene Identification
AI总结：
本文针对真实世界低质量图像描述难题，基于MIT Indoor scenes构建含800+图像、65+场景类别的数据集，并采用IC3方法生成描述，系统评估了不同编码机制、语言解码器和训练流程下多种模型的性能。
摘要：
图像描述是一项计算机视觉任务，旨在为图像生成自然语言描述。该方法在图像检索系统、医学以及多个行业等众多领域中有着广泛的应用。然而，尽管图像描述领域已有大量研究，但大多数研究都集中在高质量图像或受控环境上，并未探索真实世界图像描述所面临的挑战。真实世界图像描述涉及复杂且动态的环境，存在众多关注点，图像质量往往非常差，这使得该任务即使对人类而言也极具挑战性。本文使用一个新创建的真实世界数据集，评估了构建在不同编码机制、语言解码器和训练流程之上的多种模型的性能。该数据集基于MIT Indoor scenes数据集构建，包含超过800张图像，涵盖超过65个不同的场景类别。该数据集使用IC3方法进行描述，该方法通过从图像的独特视角总结标准图像描述模型所覆盖的细节，生成更具描述性的文字说明。
英文摘要：
Image captioning is a computer vision task that involves generating natural language descriptions for images. This method has numerous applications in various domains, including image retrieval systems, medicine, and various industries. However, while there has been significant research in image captioning, most studies have focused on high quality images or controlled environments, without exploring the challenges of real-world image captioning. Real-world image captioning involves complex and dynamic environments with numerous points of attention, with images which are often very poor in quality, making it a challenging task, even for humans. This paper evaluates the performance of various models that are built on top of different encoding mechanisms, language decoders and training procedures using a newly created real-world dataset that consists of over 800+ images of over 65 different scene classes, built using MIT Indoor scenes dataset. This dataset is captioned using the IC3 approach that generates more descriptive captions by summarizing the details that are covered by standard image captioning models from unique view-points of the image.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
