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arXiv · 2102.13086

Simple multi-dataset detection

Xingyi Zhou·Vladlen Koltun·Philipp Krähenbühl
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Abstract

How do we build a general and broad object detection system? We use all labels of all concepts ever annotated. These labels span diverse datasets with potentially inconsistent taxonomies. In this paper, we present a simple method for training a unified detector on multiple large-scale datasets. We use dataset-specific training protocols and losses, but share a common detection architecture with dataset-specific outputs. We show how to automatically integrate these dataset-specific outputs into a common semantic taxonomy. In contrast to prior work, our approach does not require manual taxonomy reconciliation. Experiments show our learned taxonomy outperforms a expert-designed taxonomy in all datasets. Our multi-dataset detector performs as well as dataset-specific models on each training domain, and can generalize to new unseen dataset without fine-tuning on them. Code is available at https://github.com/xingyizhou/UniDet.

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Xingyi Zhou, Vladlen Koltun, Philipp Krähenbühl. 2021-02-25. Simple multi-dataset detection. https://arxiv.org/abs/2102.13086

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