PPT-A SCALABLE APPROACH TO VIDEO SUMMARIZATION AND ADAPTATION

Author : celsa-spraggs | Published Date : 2017-05-30

Luis Herranz Arribas Supervisor Dr José M Martínez Sánchez Video Processing and Understanding Lab Universidad Aut ónoma de Madrid Outline Introduction Integrated

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A SCALABLE APPROACH TO VIDEO SUMMARIZATION AND ADAPTATION: Transcript


Luis Herranz Arribas Supervisor Dr José M Martínez Sánchez Video Processing and Understanding Lab Universidad Aut ónoma de Madrid Outline Introduction Integrated summarization. Maria E. Fernandez, PhD. Associate Professor of Health Promotion and Behavioral Sciences. University of Texas Health Science Center at Houston. . Who is responsible?. Researchers/ program developers, implementers, health service providers, funders?. video -Object driven Vs. Story driven. Presented By: Elad Osherov Jan 2013. Today’s talk. Motivation. Related Work. Object driven summarization. Story driven summarization. Results. Future Development. Presented by. Poulami ghosh. Assistant Professor, Department of Physical Education,. Union Christian Training College, . Berhampore, Murshidabad, W.B. .. Load. Training load feature tells us how hard. Reviews & Speech. Ling 573. Systems and Applications. May . 26, 2016. Roadmap. Abstractive summarization example. Using Abstract Meaning Representation. Review . summarization:. Basic approach. Learning what users want. (Combined Method). 1. Tatsuro. . Oya. Extractive Summarization DA Recognition. . Locate . important sentences . in email and model . dialogue . acts . simultaneously. .. 2. Outline. Introduction. A PROJECT UNDER THE GUIDANCE OF DR. K. R. RAO COURSE: EE5359 - MULTIMEDIA PROCESSING, SPRING 2015. Submitted . By. :. Aanal. Desai. UT ARLINGTON ID: 1001103728. EMAIL ID: aanal.desai@mavs.uta.edu. DEPARTMENT OF ELECTRICAL ENGINEERING UNIVERSITY OF TEXAS, ARLINGTON. Tarun . Mangla. T. , . Nawanol . Theera-Amprnount. *, . Mostafa . Ammar. T. ,. Ellen . Zegura. T. , . Saurabh. . Bagchi. *. 1. Effect . of Bandwidth Prediction Quality on Adaptive . Streaming in . Mobile Environments. Kathleen McKeown. Department of Computer Science. Columbia University. What is Summarization?. Data as input (database, software trace, expert system), text summary as output. Text as input (one or more articles), paragraph summary as output. Kathleen McKeown. Department of Computer Science. Columbia University. What is Summarization?. Data as input (database, software trace, expert system), text summary as output. Text as input (one or more articles), paragraph summary as output. Access Pipeline Protests (NoDAPL). CS 5984/4984 Big Data Text Summarization Report. . Xiaoyu Chen*, Haitao Wang, Maanav Mehrotra, Naman Chhikara, Di Sun. {xiaoyuch, wanght, maanav, namanchhikara, sdi1995} @vt.edu. Document Summarization Abhirut Gupta Mandar Joshi Piyush Dungarwal Motivation The advent of WWW has created a large reservoir of data A short summary, which conveys the essence of the document, helps in finding relevant information quickly Ameet. Deshpande. March 24, 2020. TASK. Text Summarization is the reduction of data to a (minimal) subset which represents the original data. Two types of Summarization techniques. Extractive Summarization. Kathleen McKeown. Department of Computer Science. Columbia University. Today. HW3 assigned. Summarization (switch in order of topics). WEKA tutorial (for HW3). Midterms back. What is Summarization?. Data as input (database, software trace, expert system), text summary as output. (Combined Method). 1. Tatsuro. . Oya. Extractive Summarization + DA Recognition. . Locate . important sentences . in email and model . dialogue . acts . simultaneously. .. 2. Outline. Introduction.

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