攻击泰坦 (AoT):动漫图像数据集用于角色,场景,情绪识别和超越
Guldasta Imraj Khan1, Hafeez Anwar1, Qasim Jan1
1Department of Computer Science, National University of Computer and Emerging Sciences (NUCES-FAST, Phase 1 Hayatabad, Peshawar KPK Pakistan.
Data in brief
|December 16, 2025
概括
这项研究引入了一个新的动漫角色数据集从攻击泰坦的计算机视觉研究. 该数据集有助于在动漫中识别角色和检测情绪等任务.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 数字图像分析 数字图像分析
背景情况:
- 动漫是一个全球流行的媒介,具有计算机视觉应用的巨大潜力.
- 公共可用的高质量的动漫角色数据集的稀缺性阻碍了研究进展.
- 现有的数据集往往缺乏详细的动漫分析所需的多样性和特异性.
研究的目的:
- 为了解决以动漫为重点的计算机视觉数据集的差距.
- 为了介绍攻击泰坦:动漫图像数据集的研究.
- 为了促进计算机视觉任务,如字符识别和情绪检测.
主要方法:
- 手动收集4041个高分辨率屏幕截图的泰坦袭击动漫系列.
- 将图像分为14个不同的类别,每个类别代表着一个突出的角色.
- 在数据集中包含各种角色姿势,表情,服装和背景.
主要成果:
- 创建一个新的,高质量的数据集,专门用于动漫计算机视觉研究.
- 数据集包含14个字符类中的4041个图像.
- 图像捕捉了与角色分析相关的各种各样的视觉元素.
结论:
- 攻击泰坦:动漫图像数据集为推进以动漫为重点的计算机视觉提供了宝贵的资源.
- 该数据集支持各种应用,包括字符识别,情绪检测和风格分类.
- 这个资源可以帮助计算机视觉模型的域适应技术.
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