NAS-SpatialFlow : neural architecture search for panoptic segmentation
Neural architecture search (NAS) has achieved success in various deep learning tasks. NAS can automatically find an efficient neural network architecture for a certain task on a dataset, which can outperform human-designed neural architectures. NAS has proved its efficiency in classification task (I...
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sg-ntu-dr.10356-1380132020-04-22T01:39:40Z NAS-SpatialFlow : neural architecture search for panoptic segmentation Cao, Liu Chen Change Loy School of Computer Science and Engineering SenseTime ccloy@ntu.edu.sg Engineering::Computer science and engineering Neural architecture search (NAS) has achieved success in various deep learning tasks. NAS can automatically find an efficient neural network architecture for a certain task on a dataset, which can outperform human-designed neural architectures. NAS has proved its efficiency in classification task (ImageNet). However, no previous work re- lated to NAS has been done on panoptic segmentation, a challenging task which unifies instance segmentation and semantic segmentation. In this final year project, we apply a gradient-based NAS method called DARTS to search for a network structure to bet- ter fuse features between sub-networks based on SpatiaFlow, a unified neural network with four sub-networks for panoptic segmentation. We name the final searched model NAS-SpatialFlow. Experimental results on the MS-COCO dataset show that NAS- SpatialFlow achieves 39.7 PQ, which is inferior to the human-designed model Spa- tialFlow (40.5 PQ). Our results demonstrate the ineffectiveness of the current gradient- based NAS methods in more complicated tasks like panoptic segmentation. The main contributions of this final year project are three-fold (1) We reimplement SpatialFlow as our baseline model and prove its effectiveness on panoptic segmenta- tion. (2) We apply NAS methods to search for network structures for feature fusion between sub-networks (3) We design attention modules to further enhance the recog- nition of the background stuff of SpatialFlow. Bachelor of Engineering (Computer Science) 2020-04-22T01:39:40Z 2020-04-22T01:39:40Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/138013 en SCSE19-0112 application/pdf Nanyang Technological University |
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Engineering::Computer science and engineering Cao, Liu NAS-SpatialFlow : neural architecture search for panoptic segmentation |
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Neural architecture search (NAS) has achieved success in various deep learning tasks. NAS can automatically find an efficient neural network architecture for a certain task on a dataset, which can outperform human-designed neural architectures. NAS has proved its efficiency in classification task (ImageNet). However, no previous work re- lated to NAS has been done on panoptic segmentation, a challenging task which unifies instance segmentation and semantic segmentation. In this final year project, we apply a gradient-based NAS method called DARTS to search for a network structure to bet- ter fuse features between sub-networks based on SpatiaFlow, a unified neural network with four sub-networks for panoptic segmentation. We name the final searched model NAS-SpatialFlow. Experimental results on the MS-COCO dataset show that NAS- SpatialFlow achieves 39.7 PQ, which is inferior to the human-designed model Spa- tialFlow (40.5 PQ). Our results demonstrate the ineffectiveness of the current gradient- based NAS methods in more complicated tasks like panoptic segmentation.
The main contributions of this final year project are three-fold (1) We reimplement SpatialFlow as our baseline model and prove its effectiveness on panoptic segmenta- tion. (2) We apply NAS methods to search for network structures for feature fusion between sub-networks (3) We design attention modules to further enhance the recog- nition of the background stuff of SpatialFlow. |
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Chen Change Loy |
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Chen Change Loy Cao, Liu |
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Final Year Project |
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Cao, Liu |
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Cao, Liu |
title |
NAS-SpatialFlow : neural architecture search for panoptic segmentation |
title_short |
NAS-SpatialFlow : neural architecture search for panoptic segmentation |
title_full |
NAS-SpatialFlow : neural architecture search for panoptic segmentation |
title_fullStr |
NAS-SpatialFlow : neural architecture search for panoptic segmentation |
title_full_unstemmed |
NAS-SpatialFlow : neural architecture search for panoptic segmentation |
title_sort |
nas-spatialflow : neural architecture search for panoptic segmentation |
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Nanyang Technological University |
publishDate |
2020 |
url |
https://hdl.handle.net/10356/138013 |
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1681058100171243520 |