论文标题
便宜的预训练午餐：有效的物体检测范式
Cheaper Pre-training Lunch: An Efficient Paradigm for Object Detection
论文作者
论文摘要
在本文中，我们提出了一个普遍有效的预训练范式，蒙太奇预训练，以进行对象检测。与广泛采用的Imagenet预训练相比，蒙太奇的预训练仅需要目标检测数据集，同时仅采用1/4个计算资源。要构建如此有效的范式，我们通过仔细从原始图像中提取有用的样品来降低潜在的冗余样本，以蒙太奇方式组装蒙太奇的样本，以输入和使用ERF-Adaptaptip predapative predapative-Adaptive-Adaptive-Apadaptive-pressifitive pred-Adaptive-Adaptive pred-Adaptive prep-Adaptive tepressive classification模型模型。这些设计不仅包括一种新的输入模式来改善空间利用率，还包括一个新的学习目标，以扩大预审预周化模型的有效接受场。蒙太奇预训练的效率和有效性通过在MS-COCO数据集上进行的广泛实验来验证，其中结果表明使用蒙太奇预训练的模型能够与Imagenet预培训相比，能够实现ON-PAR甚至更好的检测性能。
In this paper, we propose a general and efficient pre-training paradigm, Montage pre-training, for object detection. Montage pre-training needs only the target detection dataset while taking only 1/4 computational resources compared to the widely adopted ImageNet pre-training.To build such an efficient paradigm, we reduce the potential redundancy by carefully extracting useful samples from the original images, assembling samples in a Montage manner as input, and using an ERF-adaptive dense classification strategy for model pre-training. These designs include not only a new input pattern to improve the spatial utilization but also a novel learning objective to expand the effective receptive field of the pretrained model. The efficiency and effectiveness of Montage pre-training are validated by extensive experiments on the MS-COCO dataset, where the results indicate that the models using Montage pre-training are able to achieve on-par or even better detection performances compared with the ImageNet pre-training.
