OCTScenes：面向对象中心学习的多用途真实桌面场景数据集
OCTScenes: A Versatile Real-World Dataset of Tabletop Scenes for Object-Centric Learning
AI总结：
提出OCTScenes，一个包含5000个桌面场景、15个对象、每场景60帧360度视角的真实世界数据集，用于评估对象中心学习方法，实验显示现有方法在真实数据上仍有不足。
摘要：
人类具备以组合方式理解场景的认知能力。为了使人工智能系统具备类似的能力，对象中心学习旨在无需任何监督的情况下，从视觉场景中获取单个对象的表征。尽管近年来对象中心学习在复杂合成数据集上取得了显著进展，但在应用于复杂真实世界场景时仍面临巨大挑战。其中一个关键原因是缺乏专门针对对象中心学习的真实世界数据集。为解决这一问题，我们提出了一个名为OCTScenes的多用途真实桌面场景数据集，该数据集经过精心设计，可作为比较、评估和分析对象中心学习方法的基准。OCTScenes包含5000个桌面场景，共涉及15个对象。每个场景以60帧拍摄，覆盖360度视角。因此，OCTScenes是一个多用途基准数据集，可同时满足基于单图像、视频和多视图的对象中心学习方法的评估需求。我们在OCTScenes上对代表性的对象中心学习方法进行了广泛实验。结果表明，尽管最先进的方法在复杂合成数据集上表现优异，但在从真实世界数据中学习有意义的表征方面仍存在不足。此外，OCTScenes可作为现有方法进步的催化剂，激励它们适应真实世界场景。数据集和代码可在https://huggingface.co/datasets/Yinxuan/OCTScenes获取。
英文摘要：
Humans possess the cognitive ability to comprehend scenes in a compositional manner. To empower AI systems with similar capabilities, object-centric learning aims to acquire representations of individual objects from visual scenes without any supervision. Although recent advances in object-centric learning have made remarkable progress on complex synthesis datasets, there is a huge challenge for application to complex real-world scenes. One of the essential reasons is the scarcity of real-world datasets specifically tailored to object-centric learning. To address this problem, we propose a versatile real-world dataset of tabletop scenes for object-centric learning called OCTScenes, which is meticulously designed to serve as a benchmark for comparing, evaluating, and analyzing object-centric learning methods. OCTScenes contains 5000 tabletop scenes with a total of 15 objects. Each scene is captured in 60 frames covering a 360-degree perspective. Consequently, OCTScenes is a versatile benchmark dataset that can simultaneously satisfy the evaluation of object-centric learning methods based on single-image, video, and multi-view. Extensive experiments of representative object-centric learning methods are conducted on OCTScenes. The results demonstrate the shortcomings of state-of-the-art methods for learning meaningful representations from real-world data, despite their impressive performance on complex synthesis datasets. Furthermore, OCTScenes can serve as a catalyst for the advancement of existing methods, inspiring them to adapt to real-world scenes. Dataset and code are available at https://huggingface.co/datasets/Yinxuan/OCTScenes.
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。
