论文标题
导航视频数据海洋：YouTube视频中的座头鲸分类深度学习
Navigating an Ocean of Video Data: Deep Learning for Humpback Whale Classification in YouTube Videos
论文作者
论文摘要
人工智能（AI）授权的图像分析技术已证明图像和视频是了解座头鲸（Megaptera novaeangliae）的人口规模和动态的数据源。随着社交媒体的出现，诸如YouTube之类的平台在时空上下文中呈现出大量的视频数据，记录了全球用户的座头鲸相遇。在我们的工作中，我们专注于将YouTube视频的分类自动化为相关或无关紧要的，它们是否通过深度学习记录了真正的座头鲸相遇。我们使用在ImageNet数据集上预测的CNN-RNN体系结构将YouTube视频分类为相关或无关紧要。我们使用五倍的交叉验证来评估数据集，平均达到85.7％的精度和84.7％（无关）/ 86.6％（相关）F1得分。我们表明，深度学习可以用作时间效率的步骤，使社交媒体成为生物多样性评估的可行图像和视频数据来源。
Image analysis technologies empowered by artificial intelligence (AI) have proved images and videos to be an opportune source of data to learn about humpback whale (Megaptera novaeangliae) population sizes and dynamics. With the advent of social media, platforms such as YouTube present an abundance of video data across spatiotemporal contexts documenting humpback whale encounters from users worldwide. In our work, we focus on automating the classification of YouTube videos as relevant or irrelevant based on whether they document a true humpback whale encounter or not via deep learning. We use a CNN-RNN architecture pretrained on the ImageNet dataset for classification of YouTube videos as relevant or irrelevant. We achieve an average 85.7% accuracy, and 84.7% (irrelevant)/ 86.6% (relevant) F1 scores using five-fold cross validation for evaluation on the dataset. We show that deep learning can be used as a time-efficient step to make social media a viable source of image and video data for biodiversity assessments.
