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- Publisher Website: 10.1109/CVPR.2016.342
- Scopus: eid_2-s2.0-84986256919
- WOS: WOS:000400012303022
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Conference Paper: Multi-scale Patch Aggregation (MPA) for Simultaneous Detection and Segmentation
| Title | Multi-scale Patch Aggregation (MPA) for Simultaneous Detection and Segmentation |
|---|---|
| Authors | |
| Issue Date | 2016 |
| Citation | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, v. 2016-December, p. 3141-3149 How to Cite? |
| Abstract | © 2016 IEEE. Aiming at simultaneous detection and segmentation (SD-S), we propose a proposal-free framework, which detect and segment object instances via mid-level patches. We design a unified trainable network on patches, which is followed by a fast and effective patch aggregation algorithm to infer object instances. Our method benefits from end-to-end training. Without object proposal generation, computation time can also be reduced. In experiments, our method yields results 62.1% and 61.8% in terms of mAPr on VOC2012 segmentation val and VOC2012 SDS val, which are state-of-the-art at the time of submission. We also report results on Microsoft COCO test-std/test-dev dataset in this paper. |
| Persistent Identifier | http://hdl.handle.net/10722/281956 |
| ISSN | 2020 SCImago Journal Rankings: 4.658 |
| ISI Accession Number ID |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Liu, Shu | - |
| dc.contributor.author | Qi, Xiaojuan | - |
| dc.contributor.author | Shi, Jianping | - |
| dc.contributor.author | Zhang, Hong | - |
| dc.contributor.author | Jia, Jiaya | - |
| dc.date.accessioned | 2020-04-09T09:19:13Z | - |
| dc.date.available | 2020-04-09T09:19:13Z | - |
| dc.date.issued | 2016 | - |
| dc.identifier.citation | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, v. 2016-December, p. 3141-3149 | - |
| dc.identifier.issn | 1063-6919 | - |
| dc.identifier.uri | http://hdl.handle.net/10722/281956 | - |
| dc.description.abstract | © 2016 IEEE. Aiming at simultaneous detection and segmentation (SD-S), we propose a proposal-free framework, which detect and segment object instances via mid-level patches. We design a unified trainable network on patches, which is followed by a fast and effective patch aggregation algorithm to infer object instances. Our method benefits from end-to-end training. Without object proposal generation, computation time can also be reduced. In experiments, our method yields results 62.1% and 61.8% in terms of mAPr on VOC2012 segmentation val and VOC2012 SDS val, which are state-of-the-art at the time of submission. We also report results on Microsoft COCO test-std/test-dev dataset in this paper. | - |
| dc.language | eng | - |
| dc.relation.ispartof | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition | - |
| dc.title | Multi-scale Patch Aggregation (MPA) for Simultaneous Detection and Segmentation | - |
| dc.type | Conference_Paper | - |
| dc.description.nature | link_to_subscribed_fulltext | - |
| dc.identifier.doi | 10.1109/CVPR.2016.342 | - |
| dc.identifier.scopus | eid_2-s2.0-84986256919 | - |
| dc.identifier.volume | 2016-December | - |
| dc.identifier.spage | 3141 | - |
| dc.identifier.epage | 3149 | - |
| dc.identifier.isi | WOS:000400012303022 | - |
| dc.identifier.issnl | 1063-6919 | - |