通过两种采样策略，用更少标注样本高效训练目标检测模型。
Sampling Strategies for Efficient Training of Deep Learning Object Detection Algorithms
- 采用均匀采样和帧差采样降低冗余数据影响
- 减少标注样本量同时保持良好训练效果
- 适合视频目标检测与标注成本敏感场景
本文研究了两种采样策略，以提升深度学习目标检测模型的训练效率。基于深度学习模型的利普希茨连续性假设，第一种策略为均匀采样，旨在从目标动态的状态空间中均匀且随机地获取样本；第二种策略为帧差采样，用于探索视频中连续帧之间的时序冗余性。实验结果表明，所提出的采样策略能够生成高质量训练数据集，在显著减少人工标注样本数量的同时，仍可实现优异的训练性能。
原文摘要 · Abstract (English)
Two sampling strategies are investigated to enhance efficiency in training a deep learning object detection model. These sampling strategies are employed under the assumption of Lipschitz continuity of deep learning models. The first strategy is uniform sampling which seeks to obtain samples evenly yet randomly through the state space of the object dynamics. The second strategy of frame difference sampling is developed to explore the temporal redundancy among successive frames in a video. Experiment result indicates that these proposed sampling strategies provide a dataset that yields good training performance while requiring relatively few manually labelled samples.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。