連続物体認識のための新データセットとベンチマークCORe50
CORe50: a New Dataset and Benchmark for Continuous Object Recognition
連続学習による物体認識を評価するため、新規データセットCORe50とベンチマークを構築し、複数の連続学習シナリオに対するベースライン手法を提案した。
著者: Vincenzo Lomonaco, Davide Maltoni
分類: cs.CV, cs.AI, cs.LG, cs.RO
原文アブストラクト
Continuous/Lifelong learning of high-dimensional data streams is a challenging research problem. In fact, fully retraining models each time new data become available is infeasible, due to computational and storage issues, while na\"ive incremental strategies have been shown to suffer from catastrophic forgetting. In the context of real-world object recognition applications (e.g., robotic vision), where continuous learning is crucial, very few datasets and benchmarks are available to evaluate and compare emerging techniques. In this work we propose a new dataset and benchmark CORe50, specifically designed for continuous object recognition, and introduce baseline approaches for different continuous learning scenarios.
