FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3162181549> ?p ?o ?g. }

• W3162181549 endingPage "103161" @default.
• W3162181549 startingPage "103161" @default.
• W3162181549 abstract "Currently, video-based Sign language recognition (SLR) has been extensively studied using deep learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). In addition, using multi view attention mechanism along with CNNs could be an appealing solution that can be considered in order to make the machine interpretation process immune to finger self-occlusions. The proposed multi stream CNN mixes spatial and motion modelled video sequences to create a low dimensional feature vector at multiple stages in the CNN pipeline. Hence, we solve the view invariance problem into a video classification problem using attention model CNNs. For superior network performance during training, the signs are learned through a motion attention network thus focusing on the parts that play a major role in generating a view based paired pooling using a trainable view pair pooling network (VPPN). The VPPN, pairs views to produce a maximally distributed discriminating features from all the views for an improved sign recognition. The results showed an increase in recognition accuracies on 2D video sign language datasets. Similar results were obtained on benchmark action datasets such as NTU RGB D, MuHAVi, WEIZMANN and NUMA as there is no multi view sign language dataset except ours." @default.
• W3162181549 created "2021-05-24" @default.
• W3162181549 creator A5001251933 @default.
• W3162181549 creator A5040608966 @default.
• W3162181549 creator A5053647868 @default.
• W3162181549 date "2021-07-01" @default.
• W3162181549 modified "2023-09-27" @default.
• W3162181549 title "Multi-view motion modelled deep attention networks (M2DA-Net) for video based sign language recognition" @default.
• W3162181549 cites W1968688469 @default.
• W3162181549 cites W1984267840 @default.
• W3162181549 cites W2001690812 @default.
• W3162181549 cites W2008771257 @default.
• W3162181549 cites W2025632878 @default.
• W3162181549 cites W2039731429 @default.
• W3162181549 cites W2072096818 @default.
• W3162181549 cites W2079691664 @default.
• W3162181549 cites W2083587404 @default.
• W3162181549 cites W2146634731 @default.
• W3162181549 cites W2151703588 @default.
• W3162181549 cites W2160547390 @default.
• W3162181549 cites W2183341477 @default.
• W3162181549 cites W2293983223 @default.
• W3162181549 cites W2526041356 @default.
• W3162181549 cites W2588201159 @default.
• W3162181549 cites W2590251362 @default.
• W3162181549 cites W2594392364 @default.
• W3162181549 cites W2725123140 @default.
• W3162181549 cites W2743239999 @default.
• W3162181549 cites W2763871222 @default.
• W3162181549 cites W2775447708 @default.
• W3162181549 cites W2789359900 @default.
• W3162181549 cites W2799162093 @default.
• W3162181549 cites W2805029945 @default.
• W3162181549 cites W2827413932 @default.
• W3162181549 cites W2894942405 @default.
• W3162181549 cites W2895638065 @default.
• W3162181549 cites W2895864876 @default.
• W3162181549 cites W2898200611 @default.
• W3162181549 cites W2902726535 @default.
• W3162181549 cites W2903378016 @default.
• W3162181549 cites W2907300572 @default.
• W3162181549 cites W2912300837 @default.
• W3162181549 cites W2912302853 @default.
• W3162181549 cites W2936610935 @default.
• W3162181549 cites W2940551606 @default.
• W3162181549 cites W2963282966 @default.
• W3162181549 cites W2963441822 @default.
• W3162181549 cites W2963465695 @default.
• W3162181549 cites W2963738673 @default.
• W3162181549 cites W2964134613 @default.
• W3162181549 cites W2972940264 @default.
• W3162181549 cites W2973889980 @default.
• W3162181549 cites W2981360155 @default.
• W3162181549 cites W2998086661 @default.
• W3162181549 cites W3100993552 @default.
• W3162181549 cites W1873530254 @default.
• W3162181549 doi "https://doi.org/10.1016/j.jvcir.2021.103161" @default.
• W3162181549 hasPublicationYear "2021" @default.
• W3162181549 type Work @default.
• W3162181549 sameAs 3162181549 @default.
• W3162181549 citedByCount "2" @default.
• W3162181549 countsByYear W31621815492022 @default.
• W3162181549 countsByYear W31621815492023 @default.
• W3162181549 crossrefType "journal-article" @default.
• W3162181549 hasAuthorship W3162181549A5001251933 @default.
• W3162181549 hasAuthorship W3162181549A5040608966 @default.
• W3162181549 hasAuthorship W3162181549A5053647868 @default.
• W3162181549 hasConcept C104114177 @default.
• W3162181549 hasConcept C13280743 @default.
• W3162181549 hasConcept C134306372 @default.
• W3162181549 hasConcept C138885662 @default.
• W3162181549 hasConcept C139676723 @default.
• W3162181549 hasConcept C147168706 @default.
• W3162181549 hasConcept C153180895 @default.
• W3162181549 hasConcept C154945302 @default.
• W3162181549 hasConcept C185798385 @default.
• W3162181549 hasConcept C199360897 @default.
• W3162181549 hasConcept C205649164 @default.
• W3162181549 hasConcept C2776401178 @default.
• W3162181549 hasConcept C28490314 @default.
• W3162181549 hasConcept C33923547 @default.
• W3162181549 hasConcept C41008148 @default.
• W3162181549 hasConcept C41895202 @default.
• W3162181549 hasConcept C43521106 @default.
• W3162181549 hasConcept C50644808 @default.
• W3162181549 hasConcept C522192633 @default.
• W3162181549 hasConcept C70437156 @default.
• W3162181549 hasConcept C81363708 @default.
• W3162181549 hasConcept C82990744 @default.
• W3162181549 hasConceptScore W3162181549C104114177 @default.
• W3162181549 hasConceptScore W3162181549C13280743 @default.
• W3162181549 hasConceptScore W3162181549C134306372 @default.
• W3162181549 hasConceptScore W3162181549C138885662 @default.
• W3162181549 hasConceptScore W3162181549C139676723 @default.
• W3162181549 hasConceptScore W3162181549C147168706 @default.
• W3162181549 hasConceptScore W3162181549C153180895 @default.
• W3162181549 hasConceptScore W3162181549C154945302 @default.
• W3162181549 hasConceptScore W3162181549C185798385 @default.

• next