必要な部分だけラベル付け：部分ラベル付きシーンによる知覚・予測のための細粒度アクティブ選択
Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenes
自動運転の知覚・予測タスク向けに、コストを考慮し部分ラベル付きシーンから細かくサンプルを選ぶアクティブ学習手法を提案し、大規模実データで知覚・予測・計画の性能向上を示した。
著者: Sean Segal, Nishanth Kumar, Sergio Casas, Wenyuan Zeng, Mengye Ren, Jingkang Wang, Raquel Urtasun
分類: cs.CV, cs.AI, cs.LG, cs.RO
原文アブストラクト
Self-driving vehicles must perceive and predict the future positions of nearby actors in order to avoid collisions and drive safely. A learned deep learning module is often responsible for this task, requiring large-scale, high-quality training datasets. As data collection is often significantly cheaper than labeling in this domain, the decision of which subset of examples to label can have a profound impact on model performance. Active learning techniques, which leverage the state of the current model to iteratively select examples for labeling, offer a promising solution to this problem. However, despite the appeal of this approach, there has been little scientific analysis of active learning approaches for the perception and prediction (P&P) problem. In this work, we study active learning techniques for P&P and find that the traditional active learning formulation is ill-suited for the P&P setting. We thus introduce generalizations that ensure that our approach is both cost-aware and allows for fine-grained selection of examples through partially labeled scenes. Our experiments on a real-world, large-scale self-driving dataset suggest that fine-grained selection can improve the performance across perception, prediction, and downstream planning tasks.
