Seesaw Loss for Long-Tailed Instance Segmentation

Jiaqi Wang
Jiaqi Wang
Wenwei Zhang
Wenwei Zhang
Yuhang Zang
Yuhang Zang
Yuhang Cao
Yuhang Cao
Tao Gong
Tao Gong
Kai Chen
Kai Chen
Cited by: 2|Views35

Abstract:

This report presents the approach used in the submission of the LVIS Challenge 2020 of team MMDet. In the submission, we propose Seesaw Loss that dynamically rebalances the penalty to each category according to a relative ratio of cumulative training instances between different categories. Furthermore, we propose HTC-Lite, a light-weigh...More

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