ClickBAIT-v2：リアルタイムで物体検出器を訓練する
ClickBAIT-v2: Training an Object Detector in Real-Time
ライブ映像上で人間の最小限のクリック注釈と物体追跡を組み合わせ、物体検出器をリアルタイムに訓練するシステムを提案し、人間の介入あたりの学習効果を3〜4倍に高めた。
著者: Ervin Teng, Rui Huang, Bob Iannucci
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
Modern deep convolutional neural networks (CNNs) for image classification and object detection are often trained offline on large static datasets. Some applications, however, will require training in real-time on live video streams with a human-in-the-loop. We refer to this class of problem as time-ordered online training (ToOT). These problems will require a consideration of not only the quantity of incoming training data, but the human effort required to annotate and use it. We demonstrate and evaluate a system tailored to training an object detector on a live video stream with minimal input from a human operator. We show that we can obtain bounding box annotation from weakly-supervised single-point clicks through interactive segmentation. Furthermore, by exploiting the time-ordered nature of the video stream through object tracking, we can increase the average training benefit of human interactions by 3-4 times.
