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Author (up) Mohammed Al Rawi; Ernest Valveny edit   pdf
url  doi
  Title Compact and Efficient Multitask Learning in Vision, Language and Speech Type Conference Article
  Year 2019 Publication IEEE International Conference on Computer Vision Workshops Abbreviated Journal  
  Volume Issue Pages 2933-2942  
  Abstract Across-domain multitask learning is a challenging area of computer vision and machine learning due to the intra-similarities among class distributions. Addressing this problem to cope with the human cognition system by considering inter and intra-class categorization and recognition complicates the problem even further. We propose in this work an effective holistic and hierarchical learning by using a text embedding layer on top of a deep learning model. We also propose a novel sensory discriminator approach to resolve the collisions between different tasks and domains. We then train the model concurrently on textual sentiment analysis, speech recognition, image classification, action recognition from video, and handwriting word spotting of two different scripts (Arabic and English). The model we propose successfully learned different tasks across multiple domains.  
  Address Seul; Korea; October 2019  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICCVW  
  Notes DAG; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ RaV2019 Serial 3365  
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