MULTI-SCALE GEM POOLING WITH N-PAIR CENTER LOSS FOR FINE-GRAINED IMAGE SEARCH

Most existing fine-grained image retrieval schemes are built based upon deep feature learning paradigms, which typically leverage the feature maps of the last convolutional layer as features. However, such representation focuses only on the global information of the object, leaving the local details unexploited, which is however crucial to identifying subtle differences for fine-grained retrieval. In this paper, we have discovered that the mid-level feature map roles as local salient regions, which well complements the existing global feature representations. To this end, a multi-layer framework is proposed to integrate both local and global representations with generalized mean (GeM) pooling and attention mechanism, trained with the proposed N-pair Center loss to learn more discriminative features. By doing so, state-of-the-art performance can be achieved without using the hard or negative example minings. In the experiments, our approach outperforms favourably compared to the current state-of-the-art methods on the CUB-200-2011, CARS196 and In-shop Clothes Retrieval datasets.