CHI2014 - Crowdsourcing Step-by-Step Information Extraction to Enhance Existing How-to Videos
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The document proposes a method to enhance existing how-to videos by crowdsourcing step-by-step annotations. It presents a multi-stage crowdsourcing workflow to extract timing, labels, and before/after images for steps. An evaluation of the method on 75 videos across domains found 80% accuracy compared to expert annotations. A preliminary user study also found that a step-by-step video player improved task performance, self-efficacy, and design quality compared to a regular player.
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CHI2014 - Crowdsourcing Step-by-Step Information Extraction to Enhance Existing How-to Videos
- 1. Juho Kim Phu Nguyen Sarah Weir Philip J. Guo Robert C. Miller Krzysztof Z. Gajos Crowdsourcing Step-by-Step Information Extraction to Enhance Existing How-to Videos
- 4. learning from how-to videos: limited by video player interfaces
- 7. Problem in Watching It’s difficult to navigate to specific parts you’re interested in.
- 8. Problem in Watching It’s difficult to navigate to specific parts you’re interested in. find repeat skip
- 9. How-to Video: Step-by-Step Nature Apply gradient map
- 10. Completeness & detail of step-by-step instructions are integral to task performance. Eiriksdottir and Catrambone, 2011 Proactive & random access, semantic indices in instructional videos: better task performance and learner satisfaction Zhang et al., 2006 Interactivity can help overcome the difficulties of perception and comprehension. Stopping, starting and replaying an animation can allow reinspection. Tversky et al., 2002
- 12. Design Insight Enable step-by-step navigation with high interactivity
- 13. ToolScape: Step-aware video player
- 14. work in progress images parts with no visual progress step labels & links
- 15. enhance existing how-to videos with step-level interactivity & annotation
- 16. Research Questions Does step-by-step navigation help learners? Preliminary user study How can we annotate an existing how-to video with step-by-step information? Crowdsourcing annotation workflow
- 17. Research Questions Does step-by-step navigation help learners? Preliminary user study How can we annotate an existing how-to video with step-by-step information? Crowdsourcing annotation workflow
- 18. Study: Photoshop Design Tasks 12 novice Photoshop users manually annotated videos
- 20. With ToolScape, learners will… H1. feel more confident about their design skills. - self-efficacy gain H2. believe they produced better designs. - self-rating on designs produced H3. actually produce better designs. - external rating on designs produced
- 21. H1. Higher self-efficacy gain with ToolScape – Four 7-Likert scale questions – Mann-Whitney’s U test (Z=2.06, p<0.05), error bar: standard error 1.4 0 1 2 3 4 5 6 7 ToolScape Baseline 0.1 3.8 3.8
- 22. H2. Higher self-rating with ToolScape – One 7-Likert scale question – Mann-Whitney’s U test (Z=2.70, p<0.01), error bar: standard error 5.3 3.5 0 1 2 3 4 5 6 7 ToolScape Baseline
- 23. H3. External raters rank ToolScape designs higher. – (Ranking: Lower is better) – Wilcoxon Signed-rank test (W=317, Z=-2.79, p<0.01, r=0.29) , error bar: standard error – Krippendorff’s alpha = 0.753 5.7 7.3 0 2 4 6 8 10 12 ToolScape Baseline
- 24. Non-sequentially navigating video Step-level navigation: clicked 8.9 times per task “It is great for skipping straight to relevant portions of the tutorial.” “It was also easier to go back to parts I missed.”
- 25. Research Questions Does step-by-step navigation help learners? Preliminary user study How can we annotate an existing how-to video with step-by-step information? Crowdsourcing annotation workflow
- 26. Annotations for Step-Aware Video Player • step time • step label • before/after results
- 27. Design Goals for Annotation Method • domain-independent • existing videos • untrained annotators
- 28. Crowdsourcing
- 29. Multi-stage crowdsourcing workflow When & What are the steps? Vote & Improve Before/After the steps? FIND VERIFY EXPAND
- 30. When & What are the steps? Vote & Improve Before/After the steps? FIND VERIFY EXPAND Input video
- 31. When & What are the steps? Vote & Improve Before/After the steps? FIND VERIFY EXPAND Input video
- 32. When & What are the steps? Vote & Improve Before/After the steps? FIND VERIFY EXPAND Input video
- 33. When & What are the steps? Vote & Improve Before/After the steps? FIND VERIFY EXPAND Input video
- 34. When & What are the steps? Vote & Improve Before/After the steps? FIND VERIFY EXPAND Input video Output timeline
- 35. Stage 1. FIND candidate steps
- 36. Labeling a step
- 38. Stage 2. VERIFY steps by voting/improving
- 39. Quality control for Stage 2 • Majority voting • Breaking ties – String matching to combine “similar enough” labels – Longer string “grate three cups of cheese” > “grate cheese”
- 40. Stage 3. EXPAND with before/after images
- 41. Quality control for Stage 3 • Majority voting • Breaking ties: – Pixel diff to combine “similar enough” frames – Choose what’s closer to the step
- 42. Evaluation • Generalizable? 75 Photoshop / Cooking / Makeup videos • Accurate? precision and recall against trained annotators’ labels
- 43. Across all domains, ~80% precision and recall Domain Precision Recall Cooking 0.77 0.84 Makeup 0.74 0.77 Photoshop 0.79 0.79 All 0.77 0.81
- 44. Conceptual Level Differences • “Now apply the bronzer to your face evenly” • “Apply the bronzer to the forehead” • “Apply the bronzer to the cheekbones” • “Apply the bronzer to the jawline”
- 45. Timing is 2.7 seconds off on average Ground truth: one step every 17.3 seconds 2.7 seconds
- 46. Cost: $1.07 per minute of video • 111 HITs / video (3 workers / task) • $2.50 / video (Find + Verify) • $4.85 / video (Find + Verify + Expand) • $0.32 / step (time + label + before/after)
- 47. Contributions • Study: increased interactivity improved task performance & self-efficacy • Crowd video annotation method & Find-Verify-Expand design pattern • Evaluation: fully extracted 75 existing videos across 3 domains, 80% accuracy
- 48. hierarchical solution structure extraction Catrambone, R. The subgoal learning model: Creating better examples so that students can solve novel problems. Journal of Experimental Psychology: General, 127, (1998). Ongoing Work: Beyond low-level steps
- 49. hierarchical solution structure extraction Ongoing Work: Beyond low-level steps Learnersourcing: learners as a crowd • Motivated, qualified • Feedback loop between learners & system
- 50. Future of How-to Video Learning What if we had 1000s of fully annotated videos? • Flexible learning paths with multiple videos • Step-level search, recommendation • Patterns from multiple solutions
- 51. Crowdsourcing Step-by-Step Information Extraction to Enhance Existing How-to Videos Juho Kim MIT CSAIL juhokim@mit.edu juhokim.com Acknowledgement: This work was supported in part by Quanta Computer & the Samsung Fellowship.