HEADER MODEL FOR INSTANCE SEGMENTATION, INSTANCE SEGMENTATION MODEL, IMAGE SEGMENTATION METHOD AND APPARATUS
This application discloses a header model for instance segmentation, an instance segmentation model, an image segmentation method and apparatus, and relates to the field of artificial intelligence technologies such as computer vision and deep learning technologies. The header model includes: a targe...
Gespeichert in:
Hauptverfasser: | , , , , , , , , , |
---|---|
Format: | Patent |
Sprache: | eng ; fre ; ger |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | |
container_start_page | |
container_title | |
container_volume | |
creator | FENG, Yuan XIN, Ying ZHENG, Honghui WANG, Xiaodi YUAN, Pengcheng ZHANG, Bin PENG, Yan HAN, Shumin LONG, Xiang LIN, Shufei |
description | This application discloses a header model for instance segmentation, an instance segmentation model, an image segmentation method and apparatus, and relates to the field of artificial intelligence technologies such as computer vision and deep learning technologies. The header model includes: a target box branch, including a first branch and a second branch, where the first branch is configured to process an inputted first feature map to obtain class information and confidence of a target box, and the second branch is configured to process the first feature map to obtain location information of the target box; a mask branch, configured to process an inputted second feature map to obtain mask information; the second feature map is a feature map outputted by an ROI extraction module, and the first feature map is a feature map resulting from a pooling performed on the second feature map. The application makes the segmentation information and confidence predicted by the header more accurate, resulting in a finer segmentation result of the instance segmentation. |
format | Patent |
fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_EP3872704A2</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>EP3872704A2</sourcerecordid><originalsourceid>FETCH-epo_espacenet_EP3872704A23</originalsourceid><addsrcrecordid>eNrjZMjxcHV0cQ1S8PV3cfVRcPMPUvD0Cw5x9HN2VQh2dfd19QtxDPH099PBLgzRBpT0dXRHl3EN8fB3UXD0A-KAAMcgx5DQYB4G1rTEnOJUXijNzaDg5hri7KGbWpAfn1pckJicmpdaEu8aYGxhbmRuYOJoZEyEEgB67DZB</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>HEADER MODEL FOR INSTANCE SEGMENTATION, INSTANCE SEGMENTATION MODEL, IMAGE SEGMENTATION METHOD AND APPARATUS</title><source>esp@cenet</source><creator>FENG, Yuan ; XIN, Ying ; ZHENG, Honghui ; WANG, Xiaodi ; YUAN, Pengcheng ; ZHANG, Bin ; PENG, Yan ; HAN, Shumin ; LONG, Xiang ; LIN, Shufei</creator><creatorcontrib>FENG, Yuan ; XIN, Ying ; ZHENG, Honghui ; WANG, Xiaodi ; YUAN, Pengcheng ; ZHANG, Bin ; PENG, Yan ; HAN, Shumin ; LONG, Xiang ; LIN, Shufei</creatorcontrib><description>This application discloses a header model for instance segmentation, an instance segmentation model, an image segmentation method and apparatus, and relates to the field of artificial intelligence technologies such as computer vision and deep learning technologies. The header model includes: a target box branch, including a first branch and a second branch, where the first branch is configured to process an inputted first feature map to obtain class information and confidence of a target box, and the second branch is configured to process the first feature map to obtain location information of the target box; a mask branch, configured to process an inputted second feature map to obtain mask information; the second feature map is a feature map outputted by an ROI extraction module, and the first feature map is a feature map resulting from a pooling performed on the second feature map. The application makes the segmentation information and confidence predicted by the header more accurate, resulting in a finer segmentation result of the instance segmentation.</description><language>eng ; fre ; ger</language><subject>CALCULATING ; COMPUTING ; COUNTING ; PHYSICS</subject><creationdate>2021</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20210901&DB=EPODOC&CC=EP&NR=3872704A2$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20210901&DB=EPODOC&CC=EP&NR=3872704A2$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>FENG, Yuan</creatorcontrib><creatorcontrib>XIN, Ying</creatorcontrib><creatorcontrib>ZHENG, Honghui</creatorcontrib><creatorcontrib>WANG, Xiaodi</creatorcontrib><creatorcontrib>YUAN, Pengcheng</creatorcontrib><creatorcontrib>ZHANG, Bin</creatorcontrib><creatorcontrib>PENG, Yan</creatorcontrib><creatorcontrib>HAN, Shumin</creatorcontrib><creatorcontrib>LONG, Xiang</creatorcontrib><creatorcontrib>LIN, Shufei</creatorcontrib><title>HEADER MODEL FOR INSTANCE SEGMENTATION, INSTANCE SEGMENTATION MODEL, IMAGE SEGMENTATION METHOD AND APPARATUS</title><description>This application discloses a header model for instance segmentation, an instance segmentation model, an image segmentation method and apparatus, and relates to the field of artificial intelligence technologies such as computer vision and deep learning technologies. The header model includes: a target box branch, including a first branch and a second branch, where the first branch is configured to process an inputted first feature map to obtain class information and confidence of a target box, and the second branch is configured to process the first feature map to obtain location information of the target box; a mask branch, configured to process an inputted second feature map to obtain mask information; the second feature map is a feature map outputted by an ROI extraction module, and the first feature map is a feature map resulting from a pooling performed on the second feature map. The application makes the segmentation information and confidence predicted by the header more accurate, resulting in a finer segmentation result of the instance segmentation.</description><subject>CALCULATING</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2021</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZMjxcHV0cQ1S8PV3cfVRcPMPUvD0Cw5x9HN2VQh2dfd19QtxDPH099PBLgzRBpT0dXRHl3EN8fB3UXD0A-KAAMcgx5DQYB4G1rTEnOJUXijNzaDg5hri7KGbWpAfn1pckJicmpdaEu8aYGxhbmRuYOJoZEyEEgB67DZB</recordid><startdate>20210901</startdate><enddate>20210901</enddate><creator>FENG, Yuan</creator><creator>XIN, Ying</creator><creator>ZHENG, Honghui</creator><creator>WANG, Xiaodi</creator><creator>YUAN, Pengcheng</creator><creator>ZHANG, Bin</creator><creator>PENG, Yan</creator><creator>HAN, Shumin</creator><creator>LONG, Xiang</creator><creator>LIN, Shufei</creator><scope>EVB</scope></search><sort><creationdate>20210901</creationdate><title>HEADER MODEL FOR INSTANCE SEGMENTATION, INSTANCE SEGMENTATION MODEL, IMAGE SEGMENTATION METHOD AND APPARATUS</title><author>FENG, Yuan ; XIN, Ying ; ZHENG, Honghui ; WANG, Xiaodi ; YUAN, Pengcheng ; ZHANG, Bin ; PENG, Yan ; HAN, Shumin ; LONG, Xiang ; LIN, Shufei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_EP3872704A23</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng ; fre ; ger</language><creationdate>2021</creationdate><topic>CALCULATING</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>FENG, Yuan</creatorcontrib><creatorcontrib>XIN, Ying</creatorcontrib><creatorcontrib>ZHENG, Honghui</creatorcontrib><creatorcontrib>WANG, Xiaodi</creatorcontrib><creatorcontrib>YUAN, Pengcheng</creatorcontrib><creatorcontrib>ZHANG, Bin</creatorcontrib><creatorcontrib>PENG, Yan</creatorcontrib><creatorcontrib>HAN, Shumin</creatorcontrib><creatorcontrib>LONG, Xiang</creatorcontrib><creatorcontrib>LIN, Shufei</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>FENG, Yuan</au><au>XIN, Ying</au><au>ZHENG, Honghui</au><au>WANG, Xiaodi</au><au>YUAN, Pengcheng</au><au>ZHANG, Bin</au><au>PENG, Yan</au><au>HAN, Shumin</au><au>LONG, Xiang</au><au>LIN, Shufei</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>HEADER MODEL FOR INSTANCE SEGMENTATION, INSTANCE SEGMENTATION MODEL, IMAGE SEGMENTATION METHOD AND APPARATUS</title><date>2021-09-01</date><risdate>2021</risdate><abstract>This application discloses a header model for instance segmentation, an instance segmentation model, an image segmentation method and apparatus, and relates to the field of artificial intelligence technologies such as computer vision and deep learning technologies. The header model includes: a target box branch, including a first branch and a second branch, where the first branch is configured to process an inputted first feature map to obtain class information and confidence of a target box, and the second branch is configured to process the first feature map to obtain location information of the target box; a mask branch, configured to process an inputted second feature map to obtain mask information; the second feature map is a feature map outputted by an ROI extraction module, and the first feature map is a feature map resulting from a pooling performed on the second feature map. The application makes the segmentation information and confidence predicted by the header more accurate, resulting in a finer segmentation result of the instance segmentation.</abstract><oa>free_for_read</oa></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | |
ispartof | |
issn | |
language | eng ; fre ; ger |
recordid | cdi_epo_espacenet_EP3872704A2 |
source | esp@cenet |
subjects | CALCULATING COMPUTING COUNTING PHYSICS |
title | HEADER MODEL FOR INSTANCE SEGMENTATION, INSTANCE SEGMENTATION MODEL, IMAGE SEGMENTATION METHOD AND APPARATUS |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T08%3A16%3A38IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=FENG,%20Yuan&rft.date=2021-09-01&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3EEP3872704A2%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |