YOLACT++ Better Real-Time Instance Segmentation

We present a simple, fully-convolutional model for real-time (<inline-formula><tex-math notation="LaTeX">>30</tex-math> <mml:math><mml:mrow><mml:mo>></mml:mo><mml:mn>30</mml:mn></mml:mrow></mml:math><inline-graphic...

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Vydáno v:	IEEE transactions on pattern analysis and machine intelligence Ročník 44; číslo 2; s. 1108 - 1121
Hlavní autoři:	Bolya, Daniel, Zhou, Chong, Xiao, Fanyi, Lee, Yong Jae
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.02.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Aspect ratio Computer networks Detectors Formability Image segmentation Instance segmentation Masks Mathematical models Object detection Prototypes Real time Real-time systems Shape Stability analysis Task analysis
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
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Abstract	We present a simple, fully-convolutional model for real-time (<inline-formula><tex-math notation="LaTeX">>30</tex-math> <mml:math><mml:mrow><mml:mo>></mml:mo><mml:mn>30</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="zhou-ieq1-3014297.gif"/> </inline-formula> fps) instance segmentation that achieves competitive results on MS COCO evaluated on a single Titan Xp, which is significantly faster than any previous state-of-the-art approach. Moreover, we obtain this result after training on only one GPU . We accomplish this by breaking instance segmentation into two parallel subtasks: (1) generating a set of prototype masks and (2) predicting per-instance mask coefficients. Then we produce instance masks by linearly combining the prototypes with the mask coefficients. We find that because this process doesn't depend on repooling, this approach produces very high-quality masks and exhibits temporal stability for free. Furthermore, we analyze the emergent behavior of our prototypes and show they learn to localize instances on their own in a translation variant manner, despite being fully-convolutional. We also propose Fast NMS, a drop-in 12 ms faster replacement for standard NMS that only has a marginal performance penalty. Finally, by incorporating deformable convolutions into the backbone network, optimizing the prediction head with better anchor scales and aspect ratios, and adding a novel fast mask re-scoring branch, our YOLACT++ model can achieve 34.1 mAP on MS COCO at 33.5 fps, which is fairly close to the state-of-the-art approaches while still running at real-time.
AbstractList	We present a simple, fully-convolutional model for real-time (<inline-formula><tex-math notation="LaTeX">>30</tex-math> <mml:math><mml:mrow><mml:mo>></mml:mo><mml:mn>30</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="zhou-ieq1-3014297.gif"/> </inline-formula> fps) instance segmentation that achieves competitive results on MS COCO evaluated on a single Titan Xp, which is significantly faster than any previous state-of-the-art approach. Moreover, we obtain this result after training on only one GPU . We accomplish this by breaking instance segmentation into two parallel subtasks: (1) generating a set of prototype masks and (2) predicting per-instance mask coefficients. Then we produce instance masks by linearly combining the prototypes with the mask coefficients. We find that because this process doesn't depend on repooling, this approach produces very high-quality masks and exhibits temporal stability for free. Furthermore, we analyze the emergent behavior of our prototypes and show they learn to localize instances on their own in a translation variant manner, despite being fully-convolutional. We also propose Fast NMS, a drop-in 12 ms faster replacement for standard NMS that only has a marginal performance penalty. Finally, by incorporating deformable convolutions into the backbone network, optimizing the prediction head with better anchor scales and aspect ratios, and adding a novel fast mask re-scoring branch, our YOLACT++ model can achieve 34.1 mAP on MS COCO at 33.5 fps, which is fairly close to the state-of-the-art approaches while still running at real-time. We present a simple, fully-convolutional model for real-time ( fps) instance segmentation that achieves competitive results on MS COCO evaluated on a single Titan Xp, which is significantly faster than any previous state-of-the-art approach. Moreover, we obtain this result after training on only one GPU. We accomplish this by breaking instance segmentation into two parallel subtasks: (1) generating a set of prototype masks and (2) predicting per-instance mask coefficients. Then we produce instance masks by linearly combining the prototypes with the mask coefficients. We find that because this process doesn't depend on repooling, this approach produces very high-quality masks and exhibits temporal stability for free. Furthermore, we analyze the emergent behavior of our prototypes and show they learn to localize instances on their own in a translation variant manner, despite being fully-convolutional. We also propose Fast NMS, a drop-in 12 ms faster replacement for standard NMS that only has a marginal performance penalty. Finally, by incorporating deformable convolutions into the backbone network, optimizing the prediction head with better anchor scales and aspect ratios, and adding a novel fast mask re-scoring branch, our YOLACT++ model can achieve 34.1 mAP on MS COCO at 33.5 fps, which is fairly close to the state-of-the-art approaches while still running at real-time. We present a simple, fully-convolutional model for real-time ( fps) instance segmentation that achieves competitive results on MS COCO evaluated on a single Titan Xp, which is significantly faster than any previous state-of-the-art approach. Moreover, we obtain this result after training on only one GPU. We accomplish this by breaking instance segmentation into two parallel subtasks: (1) generating a set of prototype masks and (2) predicting per-instance mask coefficients. Then we produce instance masks by linearly combining the prototypes with the mask coefficients. We find that because this process doesn't depend on repooling, this approach produces very high-quality masks and exhibits temporal stability for free. Furthermore, we analyze the emergent behavior of our prototypes and show they learn to localize instances on their own in a translation variant manner, despite being fully-convolutional. We also propose Fast NMS, a drop-in 12 ms faster replacement for standard NMS that only has a marginal performance penalty. Finally, by incorporating deformable convolutions into the backbone network, optimizing the prediction head with better anchor scales and aspect ratios, and adding a novel fast mask re-scoring branch, our YOLACT++ model can achieve 34.1 mAP on MS COCO at 33.5 fps, which is fairly close to the state-of-the-art approaches while still running at real-time.We present a simple, fully-convolutional model for real-time ( fps) instance segmentation that achieves competitive results on MS COCO evaluated on a single Titan Xp, which is significantly faster than any previous state-of-the-art approach. Moreover, we obtain this result after training on only one GPU. We accomplish this by breaking instance segmentation into two parallel subtasks: (1) generating a set of prototype masks and (2) predicting per-instance mask coefficients. Then we produce instance masks by linearly combining the prototypes with the mask coefficients. We find that because this process doesn't depend on repooling, this approach produces very high-quality masks and exhibits temporal stability for free. Furthermore, we analyze the emergent behavior of our prototypes and show they learn to localize instances on their own in a translation variant manner, despite being fully-convolutional. We also propose Fast NMS, a drop-in 12 ms faster replacement for standard NMS that only has a marginal performance penalty. Finally, by incorporating deformable convolutions into the backbone network, optimizing the prediction head with better anchor scales and aspect ratios, and adding a novel fast mask re-scoring branch, our YOLACT++ model can achieve 34.1 mAP on MS COCO at 33.5 fps, which is fairly close to the state-of-the-art approaches while still running at real-time. We present a simple, fully-convolutional model for real-time ([Formula Omitted] fps) instance segmentation that achieves competitive results on MS COCO evaluated on a single Titan Xp, which is significantly faster than any previous state-of-the-art approach. Moreover, we obtain this result after training on only one GPU . We accomplish this by breaking instance segmentation into two parallel subtasks: (1) generating a set of prototype masks and (2) predicting per-instance mask coefficients. Then we produce instance masks by linearly combining the prototypes with the mask coefficients. We find that because this process doesn’t depend on repooling, this approach produces very high-quality masks and exhibits temporal stability for free. Furthermore, we analyze the emergent behavior of our prototypes and show they learn to localize instances on their own in a translation variant manner, despite being fully-convolutional. We also propose Fast NMS, a drop-in 12 ms faster replacement for standard NMS that only has a marginal performance penalty. Finally, by incorporating deformable convolutions into the backbone network, optimizing the prediction head with better anchor scales and aspect ratios, and adding a novel fast mask re-scoring branch, our YOLACT++ model can achieve 34.1 mAP on MS COCO at 33.5 fps, which is fairly close to the state-of-the-art approaches while still running at real-time.
Author	Xiao, Fanyi Zhou, Chong Lee, Yong Jae Bolya, Daniel
Author_xml	– sequence: 1 givenname: Daniel orcidid: 0000-0003-0223-3599 surname: Bolya fullname: Bolya, Daniel email: dbolya3@gatech.edu organization: College of Computing, Georgia Institute of Technology, Atlanta, GA, USA – sequence: 2 givenname: Chong orcidid: 0000-0002-9776-7739 surname: Zhou fullname: Zhou, Chong email: cczhou@ucdavis.edu organization: Department of Computer Science, University of California, Davis, Davis, CA, USA – sequence: 3 givenname: Fanyi orcidid: 0000-0002-9839-1139 surname: Xiao fullname: Xiao, Fanyi email: fyxiao@ucdavis.edu organization: Department of Computer Science, University of California, Davis, Davis, CA, USA – sequence: 4 givenname: Yong Jae orcidid: 0000-0001-9863-1270 surname: Lee fullname: Lee, Yong Jae email: yongjaelee@ucdavis.edu organization: Department of Computer Science, University of California, Davis, Davis, CA, USA
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/32755851$$D View this record in MEDLINE/PubMed
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ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022
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SubjectTerms	Aspect ratio Computer networks Detectors Formability Image segmentation Instance segmentation Masks Mathematical models Object detection Prototypes Real time Real-time systems Shape Stability analysis Task analysis
Title	YOLACT++ Better Real-Time Instance Segmentation
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