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Social-media Storytelling Linking Hao Wu Seamus Lawless Gareth - PowerPoint PPT Presentation

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Social-media Storytelling Linking Hao Wu Seamus Lawless Gareth Jones Francois Pitie The ADAPT Centre is funded under the SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund. Task

  1. Social-media Storytelling Linking Hao Wu Seamus Lawless Gareth Jones Francois Pitie The ADAPT Centre is funded under the SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund.

  2. • Task definition • Challenges & Solutions • Training • Searching • Result www.adaptcentre.ie

  3. www.adaptcentre.ie

  4. Tour France www.adaptcentre.ie

  5. www.adaptcentre.ie

  6. Challenges & Solutions www.adaptcentre.ie

  7. Video can’t be concluded by Lack of training data only one sentences. Challenges www.adaptcentre.ie

  8. Video segmentation Pre-train + Fine tuning + Length normalization Solutions www.adaptcentre.ie

  9. Data pre-processing www.adaptcentre.ie

  10. Images Videos Queries 32k 6.2k 60 Edinburgh Festival 66k 19k 58 Le Tour de France www.adaptcentre.ie

  11. Video Text Shot boundary detection Word level + Sentence level (Skip-Thought) Image sets Image Resnet-152 Visual embeddings Text representation www.adaptcentre.ie

  12. Model overview www.adaptcentre.ie

  13. www.adaptcentre.ie

  14. Training www.adaptcentre.ie

  15. Snow Pre-training Playful dogs People having meal Deep time Show Target information Museum of Edinburgh Highlights of Chris Froome Examples www.adaptcentre.ie

  16. A boy in a dark shirt is reading a book while sitting on a piano bench Pre-training Introducing Flickr30k ( High quality “image” - “text” pairs) www.adaptcentre.ie

  17. Collecting from source domain: • Identify keywords from query file. • Match keywords with data in the source. Model E.g. Keyword: taking selfies . Collecting from search engine: • Collect labels from online image search engine (Google and Bing) using story segments + event name as query. Target information collecting www.adaptcentre.ie

  18. Chris Froome pedaling Snow www.adaptcentre.ie

  19. Searching www.adaptcentre.ie

  20. Trade-off between consistency and accuracy 𝑆 𝑢 = 0.2* 𝑆 t−1 + 0.8 * 𝑁 𝑢 (M is the model raw output, R is the modified output) Search www.adaptcentre.ie

  21. There are 5 runs submitted. The main difference is the value of λ : Conf Run1 Run2 Run3 Run4 Run5 λ 3 5 12 20 50 Source Google+ Google Google Google Google Bing λ used in penalizing long videos; L denotes number of segments; Sig() is sigmoid function. Search www.adaptcentre.ie

  22. Results www.adaptcentre.ie

  23. Summary Quality 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Run1 Run2 Run3 Run4 Run5 Edfest Tourfrance www.adaptcentre.ie

  24. Conclusion & Future Work Target specific information are crucial. Improve video representations by applying key frame selection (or building sequence model). Build a classifier to filter crawled images to make this process automatic. www.adaptcentre.ie

  25. Thanks for listening. The ADAPT Centre is funded under the SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund.

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