发表机构
University of Genoa（热那亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对可穿戴机器人视觉Affordance分割的算力限制问题，通过分析分割头的作用增强轻量级神经网络解码器，所得模型在真实数据集上优于基准方案且计算需求低。
AI 中文摘要
在可穿戴机器人上部署深度神经网络进行视觉Affordance分割可能至关重要，因为该问题存在一些相互矛盾的方面：一方面，Affordance分割需要高水平的抽象能力，通常涉及大型模型；另一方面，可穿戴机器人上的计算资源无法支持大型模型实时运行。本文分析了分割头在泛化性能与计算成本之间权衡中的作用，所得模型在知名真实数据集上的表现优于现代基准解决方案，同时满足低计算需求。
英文摘要
The deployment of deep neural networks for visual affordance segmentation on wearable robots poses may prove critical, due to some conflicting aspects of the problem. On one hand, affordance segmentation requires high-level abstraction capabilities, that typically involve large-size models. On the other hand, computing resources hosted on wearable robots prevent to run large-size models in real-time. The paper presents an analysis of the role of the segmentation head in the trade-off between generalization performance and compute cost. The obtained models outperform modern baseline solutions in well-known, real-world datasets while meeting low computing requirements.
Journal refS. Lugani, E. Ragusa, R. Zunino, and P. Gastaldo, "Lightweight Neural Networks for Affordance Segmentation: Enhancement of the Decoder Module" in Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2023
DOI：10.1007/978-3-031-48121-5_63
