发表机构
Istituto Italiano di Tecnologia; EPFL（意大利技术研究院; 洛桑联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对可供性预测方法评估难的问题，本文提出Affordance Sheet以规范任务表述等信息，实现可供性模型的可复现基准测试与现实场景可靠评估。
AI 中文摘要
可供性预测是从多模态输入中识别智能体可对目标对象执行的潜在动作。由于问题表述异质、数据集标注不一致、实验方案报告不完整、部署条件信息有限，可供性预测方法难以评估与比较，这些局限挑战了公平基准测试与性能对比。为提升透明度，本文提出Affordance Sheet（可供性表），该文档详细说明任务表述（含输入模态）、模型架构、训练信息、数据集及实验方案。Affordance Sheet支持可供性模型在现实场景中的可复现基准测试与可靠评估，包括对新条件的泛化及人类安全性评估。
英文摘要
Affordance prediction is the identification of potential actions an agent can perform on a target object from multimodal inputs. Affordance prediction methods are difficult to evaluate and compare due to heterogeneous problem formulations, inconsistent dataset annotations, incomplete reporting of experimental protocols, and limited information about deployment conditions. These limitations challenge fair benchmarking and performance comparison. To promote transparency, we propose the Affordance Sheet, a documentation detailing task formulation with its input modalities, model architectures and training information, datasets, and experimental protocols. Affordance Sheets enable reproducible benchmarking and reliable evaluation of affordance models for real-world scenarios, including generalisation to novel conditions and human safety.
CommentsPaper accepted to Workshop on Human-Centered Multimodal Intelligence in the Wild (HCMIW) in European Conference on Computer Vision (ECCV) 2026; 18 pages, 3 figures, 7 tables. Project webpage at https://apicis.github.io/aff-sheet
