In this paper, a new method for generating object and action proposals in images and videos is proposed. It builds on activations of different convolutional layers of a pretrained CNN, combining the localization accuracy of the early layers with the high informative-ness (and hence recall) of the later layers. To this end, we build an inverse cascade that, going backward from the later to the earlier convolutional layers of the CNN, selects the most promising locations and refines them in a coarse-to-fine manner. The method is efficient, because i) it re-uses the same features extracted for detection, ii) it aggregates features using integral images, and iii) it avoids a dense evaluation of the proposals thanks to the use of the inverse coarse-to-fine cascade. The method is also accurate. We show that our DeepProposals outperform most of the previously proposed object proposal and action proposal approaches and, when plugged into a CNN-based object detector, produce state-of-the-art detection performance.

本文提出了一种新的图像和视频物体和动作提议生成方法，该方法建立在预训练的卷积神经网络不同卷积层的激活基础之上，结合了早期层的定位精度和后期层的高信息性以及召回率。该方法有效的原因是：一，使用检测中提取的相同特征；二，使用积分图像聚合特征；三，使用逆级联粗到细的方法避免了对提议的密集评估。我们在实验中证明，该方法优于大多数前人的物体和动作提议方法，并在基于CNN的对象检测器中表现出最新的检测性能。

DeepProposals: 通过级联深度卷积层进行对象和动作检测