视觉可供性预测：综述与可复现性
Visual Affordance Prediction: Survey and Reproducibility
AI总结：
本文提出统一的视觉可供性预测表述，系统综述现有方法，并引入可供性表以解决可复现性和公平性问题。
摘要：
可供性是指智能体通过摄像头观察到的、可对物体执行的潜在动作。视觉可供性预测在抓取检测、可供性分类、可供性分割和手部姿态估计等任务中有着不同的表述方式。这种表述上的多样性导致了定义的不一致，从而阻碍了方法之间的公平比较。在本文中，我们通过考虑感兴趣物体的完整信息以及智能体为完成任务而与物体进行的交互，提出了一种统一的视觉可供性预测表述。这种统一的表述使我们能够全面而系统地审视各种视觉可供性研究工作，突出方法和数据集各自的优势与局限。我们还讨论了可复现性问题，例如方法实现和实验设置细节的不可获得性，这使得视觉可供性预测的基准测试变得不公平且不可靠。为了促进透明度，我们引入了可供性表（Affordance Sheet），这是一份详细说明方法解决方案、数据集和验证的文档，以支持社区未来的可复现性和公平性。
英文摘要：
Affordances are the potential actions an agent can perform on an object, as observed by a camera. Visual affordance prediction is formulated differently for tasks such as grasping detection, affordance classification, affordance segmentation, and hand pose estimation. This diversity in formulations leads to inconsistent definitions that prevent fair comparisons between methods. In this paper, we propose a unified formulation of visual affordance prediction by accounting for the complete information on the objects of interest and the interaction of the agent with the objects to accomplish a task. This unified formulation allows us to comprehensively and systematically review disparate visual affordance works, highlighting strengths and limitations of both methods and datasets. We also discuss reproducibility issues, such as the unavailability of methods implementation and experimental setups details, making benchmarks for visual affordance prediction unfair and unreliable. To favour transparency, we introduce the Affordance Sheet, a document that details the solution, datasets, and validation of a method, supporting future reproducibility and fairness in the community.
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。
