%0 Generic %1 citeulike:14614669 %A xxx, %D 2018 %K classification detection finegrained metric zero\_shot %T RepMet: Representative-based metric learning for classification and one-shot object detection %U http://arxiv.org/abs/1806.04728 %X Distance metric learning (DML) has been successfully applied to object classification, both in the standard regime of rich training data and in the few-shot scenario, where each category is represented by only few examples. In this work, we propose a new method for DML, featuring a joint learning of the embedding space and of the data distribution of the training categories, in a single training process. Our method improves upon leading algorithms for DML-based object classification. Furthermore, it opens the door for a new task in Computer Vision - a few-shot object detection, since the proposed DML architecture can be naturally embedded as the classification head of any standard object detector. In numerous experiments, we achieve state-of-the-art classification results on a variety of fine-grained datasets, and offer the community a benchmark on the few-shot detection task, performed on the Imagenet-LOC dataset. The code will be made available upon acceptance.

@misc{citeulike:14614669, abstract = {{Distance metric learning (DML) has been successfully applied to object classification, both in the standard regime of rich training data and in the few-shot scenario, where each category is represented by only few examples. In this work, we propose a new method for DML, featuring a joint learning of the embedding space and of the data distribution of the training categories, in a single training process. Our method improves upon leading algorithms for DML-based object classification. Furthermore, it opens the door for a new task in Computer Vision - a few-shot object detection, since the proposed DML architecture can be naturally embedded as the classification head of any standard object detector. In numerous experiments, we achieve state-of-the-art classification results on a variety of fine-grained datasets, and offer the community a benchmark on the few-shot detection task, performed on the Imagenet-LOC dataset. The code will be made available upon acceptance.}}, added-at = {2019-02-27T22:23:29.000+0100}, archiveprefix = {arXiv}, author = {xxx}, biburl = {https://www.bibsonomy.org/bibtex/220771ee1efecc25d3dc976305399744d/nmatsuk}, citeulike-article-id = {14614669}, citeulike-linkout-0 = {http://arxiv.org/abs/1806.04728}, citeulike-linkout-1 = {http://arxiv.org/pdf/1806.04728}, day = 15, eprint = {1806.04728}, interhash = {f44af82921b7ec6685266b950b99f28c}, intrahash = {20771ee1efecc25d3dc976305399744d}, keywords = {classification detection finegrained metric zero\_shot}, month = jun, posted-at = {2018-07-14 21:41:15}, priority = {5}, timestamp = {2019-02-27T22:23:29.000+0100}, title = {{RepMet: Representative-based metric learning for classification and one-shot object detection}}, url = {http://arxiv.org/abs/1806.04728}, year = 2018 }