アフォーダンスセグメンテーションのための軽量ニューラルネットワーク：デコーダモジュールの改良
Lightweight Neural Networks for Affordance Segmentation: Enhancement of the Decoder Module
ウェアラブルロボット向けに、計算コストを抑えつつ高精度なアフォーダンスセグメンテーションを実現する軽量ニューラルネットワークを提案し、デコーダの役割を分析して既存手法を上回る性能を示した。
著者: Simone Lugani, Edoardo Ragusa, Rodolfo Zunino, Paolo Gastaldo
分類: cs.CV, cs.LG, cs.PF
原文アブストラクト
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.