论文标题
YOLO-您只看10647次
YOLO -- You only look 10647 times
论文作者
论文摘要
通过这项工作，我们正在解释“您只看一次”（YOLO）单阶段对象检测方法作为10647固定区域建议的并行分类。我们通过表明每个Yolos输出像素都专注于先前层的特定子区域，从而支持这种观点，这与局部区域建议相当。这种理解减少了类似YOLO的单阶段对象检测模型，基于RCNN的两阶段区域建议模型和类似于RESNET的图像分类模型之间的概念差距。此外，我们创建了交互式探索工具，以更好地视觉理解Yolo信息处理流：https：//limchr.github.io/yolo_visalization
With this work we are explaining the "You Only Look Once" (YOLO) single-stage object detection approach as a parallel classification of 10647 fixed region proposals. We support this view by showing that each of YOLOs output pixel is attentive to a specific sub-region of previous layers, comparable to a local region proposal. This understanding reduces the conceptual gap between YOLO-like single-stage object detection models, RCNN-like two-stage region proposal based models, and ResNet-like image classification models. In addition, we created interactive exploration tools for a better visual understanding of the YOLO information processing streams: https://limchr.github.io/yolo_visualization
