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In this paper, we explore how three related tasks, namely keypoint detection,description, and image retrieval can be jointly tackled using a single unifiedframework, which is trained without the need of training data with point topoint correspondences. By leveraging diverse information from sequential layersof a standard ResNet-based architecture, we are able to extract keypoints anddescriptors that encode local information using generic techniques such aslocal activation norms, channel grouping and dropping, and self-distillation.Subsequently, global information for image retrieval is encoded in anend-to-end pipeline, based on pooling of the aforementioned local responses. Incontrast to previous methods in local matching, our method does not depend onpointwise/pixelwise correspondences, and requires no such supervision at alli.e. no depth-maps from an SfM model nor manually created synthetic affinetransformations. We illustrate that this simple and direct paradigm, is able toachieve very competitive results against the state-of-the-art methods invarious challenging benchmark conditions such as viewpoint changes, scalechanges, and day-night shifting localization.

UR2KiD: Unifying Retrieval, Keypoint Detection, and Keypoint Description without Local Correspondence Supervision