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This paper introduces the first two pixel retrieval benchmarks. Pixelretrieval is segmented instance retrieval. Like semantic segmentation extendsclassification to the pixel level, pixel retrieval is an extension of imageretrieval and offers information about which pixels are related to the queryobject. In addition to retrieving images for the given query, it helps usersquickly identify the query object in true positive images and exclude falsepositive images by denoting the correlated pixels. Our user study results showpixel-level annotation can significantly improve the user experience.  Compared with semantic and instance segmentation, pixel retrieval requires afine-grained recognition capability for variable-granularity targets. To thisend, we propose pixel retrieval benchmarks named PROxford and PRParis, whichare based on the widely used image retrieval datasets, ROxford and RParis.Three professional annotators label 5,942 images with two rounds ofdouble-checking and refinement. Furthermore, we conduct extensive experimentsand analysis on the SOTA methods in image search, image matching, detection,segmentation, and dense matching using our pixel retrieval benchmarks. Resultsshow that the pixel retrieval task is challenging to these approaches anddistinctive from existing problems, suggesting that further research canadvance the content-based pixel-retrieval and thus user search experience. Thedatasets can be downloaded from\href{https://github.com/anguoyuan/Pixel_retrieval-Segmented_instance_retrieval}{thislink}.

Towards Content-based Pixel Retrieval in Revisited Oxford and Paris