While many deep convolutional neural networks show promising performance in various classification tasks, multiple objects appearing in very different sizes, shapes, and appearances cause difficulty in multi-label classification using conventional neural networks. In this paper, we introduce a dual aggregated network on pyramidal convolutional features for multi-label classification. The proposed method includes both feature- and classifier-level aggregation to learn discriminant multi-scale information of various target objects in the image. First, the feature-level aggregation collects the convolutional activation maps from the multi-scale pyramid network, and then it densely pools them to take localized features of each object. We elaborately design the feature aggregation method so that the responses from the objects with different sizes, aspect ratios, and shapes are properly reflected the aggregated activation map …