2nd Place Solution to Instance Segmentation of IJCAI 3D AI Challenge 2020
Kai Jiang (1), Xiangyue Liu (2), Zheng Ju (3), Xiang Luo (1)((1), LinkDoc Technology, Beijing, China, (2) School of Software, Beihang, University, Beijing, China, (3) Huaxin consulting Co., Ltd, Hangzhou, China)

TL;DR
This paper presents a high-performing ensemble method for instance segmentation in the IJCAI 3D AI Challenge 2020, utilizing advanced models and training tricks to improve boundary accuracy on large objects.
Contribution
The authors develop an ensemble of PointRend-based Mask R-CNN models with ResNeSt, FPN, and DCNv2, achieving second place in the challenge.
Findings
Ensemble of four models achieved top performance.
Multi-scale training and test augmentation improved results.
PointRend enhanced boundary quality for large objects.
Abstract
Compared with MS-COCO, the dataset for the competition has a larger proportion of large objects which area is greater than 96x96 pixels. As getting fine boundaries is vitally important for large object segmentation, Mask R-CNN with PointRend is selected as the base segmentation framework to output high-quality object boundaries. Besides, a better engine that integrates ResNeSt, FPN and DCNv2, and a range of effective tricks that including multi-scale training and test time augmentation are applied to improve segmentation performance. Our best performance is an ensemble of four models (three PointRend-based models and SOLOv2), which won the 2nd place in IJCAI-PRICAI 3D AI Challenge 2020: Instance Segmentation.
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Taxonomy
TopicsAdvanced Neural Network Applications · Medical Imaging and Analysis · Advanced Image and Video Retrieval Techniques
MethodsRegion Proposal Network · Average Pooling · *Communicated@Fast*How Do I Communicate to Expedia? · Dense Connections · guidence~How to file a complaint against Expedia? · Global Average Pooling · Feedforward Network · Batch Normalization · Split Attention · Softmax
