Gated Square-Root Pooling for Image Instance Retrieval

Recently Convolutional Neural Networks (CNNs) have achieved great success in different fields including image instance retrieval. However traditional global pooling approaches fail to capture all possible discriminative information of CNN activations and treat activations over channels equally regardless of the different importance between channels. In this work, we focus on the mentioned problem of global feature pooling over CNN activations for image instance retrieval. We make two contributions. First, we introduce a channel-wise SQUare-root (SQU) pooling (2-norm) approach, which makes better use of information over activation maps and is superior to Average (1-norm) and Max pooling (infinity norm), in the context of instance retrieval. Second, we further improve SQU by learning a gating function that weights the contributions of different channels, in an end-to-end manner. Extensive experiments on 6 benchmark datasets show that the proposed strategies achieve considerable improvements over state-of-the-art.