Weakly Supervised Soft-detection-based Aggregation Method for Image Retrieval
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In recent year, the compact representations based on activations of Convolutional Neural Network (CNN) achieve remarkable performance in image retrieval. Some interested object only takes up a small part of the whole image. Therefore, it is significant to extract the discriminative representations that contain regional information of pivotal small object. In this paper, we propose a novel weakly supervised soft-detection-based aggregation (SDA) method free from bounding box annotations for image retrieval. In order to highlight the certain discriminative pattern of objects and suppress the noise of background, we employ trainable soft region proposals that indicate the probability of interested object and reflect the significance of candidate regions. We conduct comprehensive experiments on standard image retrieval datasets. Our weakly supervised SDA method achieves state-of-the-art performance on most benchmarks. The results demonstrate that the proposed SDA method is effective for image retrieval.
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