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Copy-Paste has proven to be a very effective data augmentation for instancesegmentation which can improve the generalization of the model. We used atask-specific Copy-Paste data augmentation method to achieve good performanceon the instance segmentation track of the 2nd VIPriors workshop challenge. Wealso applied additional data augmentation techniques including RandAugment andGridMask. Our segmentation model is the HTC detector on the CBSwin-B with CBFPNwith some tweaks. This model was trained at the multi-scale mode by a randomsampler on the 6x schedule and tested at the single-scale mode. By combiningthese techniques, we achieved 0.398 AP@0.50:0.95 with the validation set and0.433 AP@0.50:0.95 with the test set. Finally, we reached 0.477 AP@0.50:0.95with the test set by adding the validation set to the training data. Sourcecode is available at https://github.com/jahongir7174/VIP2021.

Instance Segmentation Challenge Track Technical Report, VIPriors  Workshop at ICCV 2021: Task-Specific Copy-Paste Data Augmentation Method for  Instance Segmentation