  Occlusion is a long-standing problem in computer vision, particularly in
instance segmentation. ACM MMSports 2023 DeepSportRadar has introduced a
dataset that focuses on segmenting human subjects within a basketball context
and a specialized evaluation metric for occlusion scenarios. Given the modest
size of the dataset and the highly deformable nature of the objects to be
segmented, this challenge demands the application of robust data augmentation
techniques and wisely-chosen deep learning architectures. Our work (ranked 1st
in the competition) first proposes a novel data augmentation technique, capable
of generating more training samples with wider distribution. Then, we adopt a
new architecture - Hybrid Task Cascade (HTC) framework with CBNetV2 as backbone
and MaskIoU head to improve segmentation performance. Furthermore, we employ a
Stochastic Weight Averaging (SWA) training strategy to improve the model's
generalization. As a result, we achieve a remarkable occlusion score (OM) of
0.533 on the challenge dataset, securing the top-1 position on the leaderboard.
Source code is available at this
https://github.com/nguyendinhson-kaist/MMSports23-Seg-AutoID.
