The advancement in computing power has significantly reduced the training times for deep learning, fostering the rapid development of networks designed for object recognition. However, the exploration of object utility, which is the affordance of the object, as opposed to object recognition, has received comparatively less attention. This work focuses on the problem of exploration of object affordances using existing networks trained on the object classification dataset. While pre-trained networks have proven to be instrumental in transfer learning for classification tasks, this work diverges from conventional object classification methods. Instead, it employs pre-trained networks to discern affordance labels without the need for specialized layers, abstaining from modifying the final layers through the addition of classification layers. To facilitate the determination of affordance labels without such modifications, two approaches, i.e. subspace clustering and manifold curvature methods are tested. These methods offer a distinct perspective on affordance label recognition. Especially, manifold curvature method has been successfully tested with nine distinct pre-trained networks, each achieving an accuracy exceeding 95%. Moreover, it is observed that manifold curvature and subspace clustering methods explore affordance labels that are not marked in the ground truth, but object affords in various cases.

翻译：计算能力的显著提升大幅缩短了深度学习模型的训练时间，推动了面向物体识别的网络架构的快速发展。然而，与物体识别相比，对物体效用（即物体的可供性）的探索研究相对较少。本研究聚焦于利用在物体分类数据集上预训练的网络进行物体可供性探索的问题。尽管预训练网络在分类任务的迁移学习中已被证明具有重要价值，但本研究与传统物体分类方法不同：我们直接使用预训练网络识别可供性标签，无需添加专用分类层，也无需修改网络末端结构。为实现不依赖网络修改的可供性标签判定，本文测试了子空间聚类与流形曲率两种方法。这些方法为可供性标签识别提供了独特的研究视角。特别地，流形曲率方法已在九种不同的预训练网络上成功验证，各网络准确率均超过95%。此外，研究发现流形曲率与子空间聚类方法能够探索出未在标注数据中标记、但物体在实际场景中具备的多种可供性标签。