STD2P: RGBD Semantic Segmentation Using Spatio-Temporal Data-Driven Pooling

He, Yang; Chiu, Wei-Chen; Keuper, Margret; Fritz, Mario

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STD2P: RGBD Semantic Segmentation Using Spatio-Temporal Data-Driven Pooling

He, Y., Chiu, W.-C., Keuper, M., & Fritz, M. (in press). STD2P: RGBD Semantic Segmentation Using Spatio-Temporal Data-Driven Pooling. In 30th IEEE Conference on Computer Vision and Pattern Recognition.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-002D-B8E6-C Version Permalink: https://hdl.handle.net/11858/00-001M-0000-002D-B8E7-A

Genre: Conference Paper

Latex : {STD2P}: {RGBD} Semantic Segmentation Using Spatio-Temporal Data-Driven Pooling

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Creators:
He, Yang¹, Author
Chiu, Wei-Chen¹, Author
Keuper, Margret¹, Author
Fritz, Mario¹, Author

Affiliations:
1Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society, ou_1116547

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Language(s): eng - English

Dates: Accepted: 2017

Publication Status: Accepted / In Press

Pages: 20 p.

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Identifiers: BibTex Citekey: yang_cvpr17

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Title: 30th IEEE Conference on Computer Vision and Pattern Recognition

Place of Event: Honolulu, HI, USA

Start-/End Date: 2017-07-22 - 2017-07-25

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Title: 30th IEEE Conference on Computer Vision and Pattern Recognition

Abbreviation : CVPR 2017

Source Genre: Proceedings

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