CatFLW：野外猫面部关键点数据集
CatFLW: Cat Facial Landmarks in the Wild Dataset
AI总结：
本文提出CatFLW数据集，包含2016张猫脸图像和48个关键点标注，并介绍了一种半监督标注方法，显著减少标注时间，推动动物面部分析自动化。
摘要：
动物情感计算是一个快速发展的研究领域，直到最近才出现超越动物追踪、识别其内部状态（如疼痛和情绪）的初步努力。在大多数哺乳动物中，面部表情是传达这些状态信息的重要渠道。然而，与人类领域不同，目前严重缺乏使动物面部分析自动化成为可能的数据集。本文旨在通过提出一个名为“野外猫面部关键点”（CatFLW）的数据集来填补这一空白，该数据集包含2016张不同环境和条件下的猫脸图像，并标注了48个面部关键点，这些关键点专门根据其与底层肌肉的关系以及与猫特定面部动作单元（CatFACS）的相关性而选择。据我们所知，该数据集是现有猫面部关键点数量最多的数据集。此外，我们描述了一种半监督（人在回路中）的图像关键点标注方法，用于创建该数据集，该方法显著减少了标注时间，并可用于为其他动物创建类似数据集。该数据集可根据要求提供。
英文摘要：
Animal affective computing is a quickly growing field of research, where only recently first efforts to go beyond animal tracking into recognizing their internal states, such as pain and emotions, have emerged. In most mammals, facial expressions are an important channel for communicating information about these states. However, unlike the human domain, there is an acute lack of datasets that make automation of facial analysis of animals feasible. This paper aims to fill this gap by presenting a dataset called Cat Facial Landmarks in the Wild (CatFLW) which contains 2016 images of cat faces in different environments and conditions, annotated with 48 facial landmarks specifically chosen for their relationship with underlying musculature, and relevance to cat-specific facial Action Units (CatFACS). To the best of our knowledge, this dataset has the largest amount of cat facial landmarks available. In addition, we describe a semi-supervised (human-in-the-loop) method of annotating images with landmarks, used for creating this dataset, which significantly reduces the annotation time and could be used for creating similar datasets for other animals. The dataset is available on request.
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。
