Cross-dataset Training for Class Increasing Object Detection

• URL: http://arxiv.org/abs/2001.04621v1
• Date: Tue, 14 Jan 2020 04:40:47 GMT
• Title: Cross-dataset Training for Class Increasing Object Detection
• Authors: Yongqiang Yao, Yan Wang, Yu Guo, Jiaojiao Lin, Hongwei Qin, Junjie Yan
• Abstract summary: We present a conceptually simple, flexible and general framework for cross-dataset training in object detection. By cross-dataset training, existing datasets can be utilized to detect the merged object classes with a single model. While using cross-dataset training, we only need to label the new classes on the new dataset.
• Score: 52.34737978720484
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a conceptually simple, flexible and general framework for cross-dataset training in object detection. Given two or more already labeled datasets that target for different object classes, cross-dataset training aims to detect the union of the different classes, so that we do not have to label all the classes for all the datasets. By cross-dataset training, existing datasets can be utilized to detect the merged object classes with a single model. Further more, in industrial applications, the object classes usually increase on demand. So when adding new classes, it is quite time-consuming if we label the new classes on all the existing datasets. While using cross-dataset training, we only need to label the new classes on the new dataset. We experiment on PASCAL VOC, COCO, WIDER FACE and WIDER Pedestrian with both solo and cross-dataset settings. Results show that our cross-dataset pipeline can achieve similar impressive performance simultaneously on these datasets compared with training independently.

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