Matches in SemOpenAlex for { <https://semopenalex.org/work/W3002440287> ?p ?o ?g. }
Showing items 1 to 77 of
77
with 100 items per page.
- W3002440287 abstract "Although Convolutional Neural Networks are effective visual models that generate hierarchies of features, there still exist some shortcomings in the application of Deep Convolutional Neural Networks to semantic image segmentation. In this work, our algorithm incorporates multi-scale atrous convolution, attention model and Conditional Random Fields to tackle this problem. Firstly, our method replaces deconvolutional layers with atrous convolutional layers to avoid reducing feature resolution when the Deep Convolutional Neural Networks is employed in a fully convolutional fashion. Secondly, multi-scale architecture and attention model are used to extract the existence of features at multiple scales. Thirdly, we use Conditional Random Fields to prevent the built-in invariance of Deep Convolutional Neural Networks reducing localization accuracy. Moreover, our network completely integrates Conditional Random Fields modelling with Deep Convolutional Neural Networks, making it possible to train the deep network end-to-end. In this paper, our method is used to the matters of semantic image segmentation and is demonstrated the effectiveness of our model with experiments on PASCAL VOC 2012." @default.
- W3002440287 created "2020-01-30" @default.
- W3002440287 creator A5017888848 @default.
- W3002440287 creator A5051873577 @default.
- W3002440287 creator A5087373888 @default.
- W3002440287 date "2019-11-27" @default.
- W3002440287 modified "2023-09-24" @default.
- W3002440287 title "Multi-Scale Deep Convolutional Nets with Attention Model and Conditional Random Fields for Semantic Image Segmentation" @default.
- W3002440287 cites W1495267108 @default.
- W3002440287 cites W1610060839 @default.
- W3002440287 cites W1948751323 @default.
- W3002440287 cites W1953465585 @default.
- W3002440287 cites W2022508996 @default.
- W3002440287 cites W2054279472 @default.
- W3002440287 cites W2068730032 @default.
- W3002440287 cites W2088049833 @default.
- W3002440287 cites W2110158442 @default.
- W3002440287 cites W2124592697 @default.
- W3002440287 cites W2141125852 @default.
- W3002440287 cites W2161004254 @default.
- W3002440287 cites W2168356304 @default.
- W3002440287 cites W2221898772 @default.
- W3002440287 cites W2412782625 @default.
- W3002440287 cites W2535516436 @default.
- W3002440287 cites W2545985378 @default.
- W3002440287 cites W2963563573 @default.
- W3002440287 cites W78159342 @default.
- W3002440287 doi "https://doi.org/10.1145/3372806.3372811" @default.
- W3002440287 hasPublicationYear "2019" @default.
- W3002440287 type Work @default.
- W3002440287 sameAs 3002440287 @default.
- W3002440287 citedByCount "2" @default.
- W3002440287 countsByYear W30024402872021 @default.
- W3002440287 countsByYear W30024402872022 @default.
- W3002440287 crossrefType "proceedings-article" @default.
- W3002440287 hasAuthorship W3002440287A5017888848 @default.
- W3002440287 hasAuthorship W3002440287A5051873577 @default.
- W3002440287 hasAuthorship W3002440287A5087373888 @default.
- W3002440287 hasConcept C115961682 @default.
- W3002440287 hasConcept C124504099 @default.
- W3002440287 hasConcept C152565575 @default.
- W3002440287 hasConcept C153180895 @default.
- W3002440287 hasConcept C154945302 @default.
- W3002440287 hasConcept C205649164 @default.
- W3002440287 hasConcept C2778755073 @default.
- W3002440287 hasConcept C41008148 @default.
- W3002440287 hasConcept C58640448 @default.
- W3002440287 hasConcept C81363708 @default.
- W3002440287 hasConcept C89600930 @default.
- W3002440287 hasConceptScore W3002440287C115961682 @default.
- W3002440287 hasConceptScore W3002440287C124504099 @default.
- W3002440287 hasConceptScore W3002440287C152565575 @default.
- W3002440287 hasConceptScore W3002440287C153180895 @default.
- W3002440287 hasConceptScore W3002440287C154945302 @default.
- W3002440287 hasConceptScore W3002440287C205649164 @default.
- W3002440287 hasConceptScore W3002440287C2778755073 @default.
- W3002440287 hasConceptScore W3002440287C41008148 @default.
- W3002440287 hasConceptScore W3002440287C58640448 @default.
- W3002440287 hasConceptScore W3002440287C81363708 @default.
- W3002440287 hasConceptScore W3002440287C89600930 @default.
- W3002440287 hasLocation W30024402871 @default.
- W3002440287 hasOpenAccess W3002440287 @default.
- W3002440287 hasPrimaryLocation W30024402871 @default.
- W3002440287 hasRelatedWork W1803059841 @default.
- W3002440287 hasRelatedWork W2005476934 @default.
- W3002440287 hasRelatedWork W2150908720 @default.
- W3002440287 hasRelatedWork W2897195263 @default.
- W3002440287 hasRelatedWork W2918895799 @default.
- W3002440287 hasRelatedWork W2966354721 @default.
- W3002440287 hasRelatedWork W2979932740 @default.
- W3002440287 hasRelatedWork W2994948129 @default.
- W3002440287 hasRelatedWork W3093612317 @default.
- W3002440287 hasRelatedWork W3095523211 @default.
- W3002440287 isParatext "false" @default.
- W3002440287 isRetracted "false" @default.
- W3002440287 magId "3002440287" @default.
- W3002440287 workType "article" @default.