The Second Place Solution for ICCV2021 VIPriors Instance Segmentation Challenge

• URL: http://arxiv.org/abs/2112.01072v1
• Date: Thu, 2 Dec 2021 09:23:02 GMT
• Title: The Second Place Solution for ICCV2021 VIPriors Instance Segmentation Challenge
• Authors: Bo Yan, Fengliang Qi, Leilei Cao and Hongbin Wang
• Abstract summary: The Visual Inductive Priors(VIPriors) for Data-Efficient Computer Vision challenges ask competitors to train models from scratch in a data-deficient setting. We introduce the technical details of our submission to the ICCV 2021 VIPriors instance segmentation challenge. Our approach can achieve 40.2%AP@0.50:0.95 on the test set of ICCV 2021 VIPriors instance segmentation challenge.
• Score: 6.087398773657721
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Visual Inductive Priors(VIPriors) for Data-Efficient Computer Vision challenges ask competitors to train models from scratch in a data-deficient setting. In this paper, we introduce the technical details of our submission to the ICCV2021 VIPriors instance segmentation challenge. Firstly, we designed an effective data augmentation method to improve the problem of data-deficient. Secondly, we conducted some experiments to select a proper model and made some improvements for this task. Thirdly, we proposed an effective training strategy which can improve the performance. Experimental results demonstrate that our approach can achieve a competitive result on the test set. According to the competition rules, we do not use any external image or video data and pre-trained weights. The implementation details above are described in section 2 and section 3. Finally, our approach can achieve 40.2\%AP@0.50:0.95 on the test set of ICCV2021 VIPriors instance segmentation challenge.

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