2nd Place Solution to Instance Segmentation of IJCAI 3D AI Challenge 2020
원문 초록
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.
함께 살펴볼 논문
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NOCTIS: Novel Object Cyclic Threshold based Instance Segmentation
No time to train! Training-Free Reference-Based Instance Segmentation
SeC: Advancing Complex Video Object Segmentation via Progressive Concept Construction
SCORE: Scene Context Matters in Open-Vocabulary Remote Sensing Instance Segmentation
MUVOD: A Novel Multi-view Video Object Segmentation Dataset and A Benchmark for 3D Segmentation
DreamGrasp: Zero-Shot 3D Multi-Object Reconstruction from Partial-View Images for Robotic Manipulation
토론
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