GRM: Gradient Rectification Module for Visual Place Retrieval
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Visual place retrieval aims to search images in the database that depict similar places as the query image. However, global descriptors encoded by the network usually fall into a low dimensional principal space, which is harmful to the retrieval performance. We first analyze the cause of this phenomenon, pointing out that it is due to degraded distribution of the gradients of descriptors. Then, a new module called Gradient Rectification Module(GRM) is proposed to alleviate this issue. It can be appended after the final pooling layer. This module can rectify the gradients to the complement space of the principal space. Therefore, the network is encouraged to generate descriptors more uniformly in the whole space. At last, we conduct experiments on multiple datasets and generalize our method to classification task under prototype learning framework.
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