Learning a Complete Image Indexing Pipeline
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Abstract To work at scale, a complete image indexing system comprises two components: An inverted fifile index to restrict the actual search to only a subset that should contain most of the items relevant to the query; An approximate distance computation mechanism to rapidly scan these lists. While supervised deep learning has recently enabled improvements to the latter, the former continues to be based on unsupervised clustering in the literature. In this work, we propose a fifirst system that learns both components within a unifying neural framework of structured binary encoding
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