Feature representation learning on multi-scale receptive fields for objection recognition

In this paper, we have proposed a novel feature representation on multi-scale receptive fields for objection recognition. The method is based on a modified convolutional neural networks (CNN), named network-in-network (NIN), which has shown a good performance in some computer vision tasks. However, applying NIN to some specific applications may encounter a few problems. First, the NIN removes the fully connected layers, which makes it unsuited to use in large-scale face recognition due to lack of an efficient feature representation, even though it brings a lot of performance benefits. Second, some lowerlayer features, which can make the feature representation more discriminative, is unused. In the pure forward architecture, these features are unseen to the classifier. To solve the two problems, we present a multi-scale receptive fields (MSRF) representation learning scheme. Based on a well trained NIN, we add a pathway to top layer and design a feature vector as final representation. In our experiments, we compare the result of our multi-scale receptive fields with standard NIN architecture. The results show our method can obtain a more explicit feature representation and improvements in performance.