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Видео про космос 2 класс
содержание презентации «Видео про космос 2 класс.ppt»| Сл | Текст | Сл | Текст |
| 1 | Grouplet: A Structured Image | 15 | 2-Grouplet). Gaussian distribution. Visual |
| Representation for Recognizing Human and | codewords. Notations. I: Image. P: | ||
| Object Interactions. Bangpeng Yao and Li | Reference point in the image. ?: Grouplet. | ||
| Fei-Fei Computer Science Department, | ?i: Feature unit. ?(?,I): Matching score | ||
| Stanford University. | of ? and I. ?(?i,I): Matching score of ?i | ||
| {bangpeng,feifeili}@cs.stanford.edu. 1. | and I. For an image patch: ?(x): Image | ||
| 2 | Human-Object Interaction. Playing | neighborhood of x. ?: A small shift of the | |
| saxophone. Human. Not playing saxophone. | location. Matching score between ? and I. | ||
| Saxophone. 2. | Matching score between ?i and I. Codeword | ||
| 3 | Human-Object Interaction. Robots | assignment score. Gaussian density value. | |
| interact with objects. Automatic sports | Codeword assignment score. Gaussian | ||
| commentary. Medical care. “Kobe is dunking | density value. Ai: Visual codeword; xi: | ||
| the ball.”. 3. | Image location; ?i: Variance of spatial | ||
| 4 | Background: Human-Object Interaction. | distribution. a?: Its visual appearance; | |
| To be done. context. Schneiderman & | x?: Its image location. 15. | ||
| Kanade, 2000 Viola & Jones, 2001 Huang | 16 | Grouplet representation. Playing | |
| et al, 2007 Papageorgiou & Poggio, | saxophone. Other interactions. Part-based | ||
| 2000 Wu & Nevatia, 2005 Dalal & | configuration Co-occurrence | ||
| Triggs, 2005 Mikolajczyk et al, 2005 Leibe | Discriminative. matching score: 0.6. | ||
| et al, 2005 Bourdev & Malik, 2009 | matching score: 0.4. matching score: 0.0. | ||
| Felzenszwalb & Huttenlocher, 2005 Ren | matching score: 0.1. 16. | ||
| et al, 2005 Ramanan, 2006 Ferrari et al, | 17 | Grouplet representation. Part-based | |
| 2008 Yang & Mori, 2008 Andriluka et | configuration Co-occurrence Discriminative | ||
| al, 2009 Eichner & Ferrari, 2009. | Dense. All possible combinations of | ||
| Lowe, 1999 Belongie et al, 2002 Fergus et | feature units. Densely sample image | ||
| al, 2003 Fei-Fei et al, 2004 Berg & | locations. Many possible spatial | ||
| Malik, 2005 Felzenszwalb et al, 2005 | distributions. All possible Codewords. | ||
| Grauman & Darrell, 2005 Sivic et al, | 1-grouplet. 2-grouplet. 3-grouplet. 17. | ||
| 2005 Lazebnik et al, 2006 Zhang et al, | 18 | Outline. Intuition of Grouplet | |
| 2006 Savarese et al, 2007 Lampert et al, | Representation Grouplet Feature | ||
| 2008 Desai et al, 2009 Gehler & | Representation Using Grouplet for | ||
| Nowozin, 2009. Gupta et al, 2009. Yao | Recognition Dataset & Experiments | ||
| & Fei-Fei, 2010a. Yao & Fei-Fei, | Conclusion. 18. | ||
| 2010b. Murphy et al, 2003 Hoiem et al, | 19 | A “Space” of Grouplets. 19. | |
| 2006 Shotton et al, 2006. Rabinovich et | 20 | A “Space” of Grouplets. 20. | |
| al, 2007 Heitz & Koller, 2008 Divvala | 21 | A “Space” of Grouplets. 21. | |
| et al, 2009. vs. 4. | 22 | A “Space” of Grouplets. On background. | |
| 5 | Background: Human-Object Interaction. | Shared by different interactions. 22. | |
| To be done. context. Schneiderman & | 23 | We only need discriminative Grouplets. | |
| Kanade, 2000 Viola & Jones, 2001 Huang | Number of feature units: N. N is large | ||
| et al, 2007 Papageorgiou & Poggio, | (192200). Number of Grouplets: 2N very | ||
| 2000 Wu & Nevatia, 2005 Dalal & | large space. On background. Shared by | ||
| Triggs, 2005 Mikolajczyk et al, 2005 Leibe | different interactions. Large ?(?,I). | ||
| et al, 2005 Bourdev & Malik, 2009 | Small ?(?,I). Large ?(?,I). Small ?(?,I). | ||
| Felzenszwalb & Huttenlocher, 2005 Ren | 23. 23. | ||
| et al, 2005 Ramanan, 2006 Ferrari et al, | 24 | Obtaining discriminative grouplets for | |
| 2008 Yang & Mori, 2008 Andriluka et | a class. Apriori Mining. Mine 1000~2000 | ||
| al, 2009 Eichner & Ferrari, 2009. | grouplets, only need to evaluate (2~100)?N | ||
| Lowe, 1999 Belongie et al, 2002 Fergus et | grouplets. Obtain grouplets with large | ||
| al, 2003 Fei-Fei et al, 2004 Berg & | ?(?,I) on the class. Remove grouplets with | ||
| Malik, 2005 Felzenszwalb et al, 2005 | large ?(?,I) from other classes. Number of | ||
| Grauman & Darrell, 2005 Sivic et al, | feature units: N. N is large (192200). | ||
| 2005 Lazebnik et al, 2006 Zhang et al, | Number of Grouplets: 2N very large space. | ||
| 2006 Savarese et al, 2007 Lampert et al, | Selected 1-grouplets. Candidate | ||
| 2008 Desai et al, 2009 Gehler & | 2-grouplets. [Agrawal & Srikant, | ||
| Nowozin, 2009. Gupta et al, 2009. Yao | 1994]. 24. | ||
| & Fei-Fei, 2010a. Yao & Fei-Fei, | 25 | Using Grouplets for Classification. | |
| 2010b. Murphy et al, 2003 Hoiem et al, | SVM. Discriminative grouplets. 25. | ||
| 2006 Shotton et al, 2006. Rabinovich et | 26 | Outline. Intuition of Grouplet | |
| al, 2007 Heitz & Koller, 2008 Divvala | Representation Grouplet Feature | ||
| et al, 2009. vs. 5. | Representation Using Grouplet for | ||
| 6 | Outline. Intuition of Grouplet | Recognition Dataset & Experiments | |
| Representation Grouplet Feature | Conclusion. 26. | ||
| Representation Using Grouplet for | 27 | People-Playing-Musical-Instruments | |
| Recognition Dataset & Experiments | (PPMI) Dataset. | ||
| Conclusion. 6. | http://vision.stanford.edu/resources_links | ||
| 7 | Outline. Intuition of Grouplet | html. PPMI+. PPMI-. Normalized image (200 | |
| Representation Grouplet Feature | images each interaction). Original image. | ||
| Representation Using Grouplet for | # Image: # Image: (172). (191). (177). | ||
| Recognition Dataset & Experiments | (179). (200). (198). (185). (133). (149). | ||
| Conclusion. 7. | (188). (167). (148). (169). (164). 27. | ||
| 8 | Recognizing Human-Object Interaction | 28 | Recognition Tasks on |
| is Challenging. Reference image: playing | People-Playing-Musical-Instruments (PPMI) | ||
| saxophone. Different pose (or viewpoint). | Dataset. Classification. Detection. | ||
| Different lighting. Different background. | Playing different instruments. Playing vs. | ||
| Different instrument, similar pose. Same | Not playing. For each interaction, 100 | ||
| object (saxophone), different | training and 100 testing images. vs. vs. | ||
| interactions. 8. | 28. | ||
| 9 | Grouplet: our intuition. Bag-of-words. | 29 | Classification: Playing Different |
| Spatial pyramid. Part-based. Grouplet | Instruments. 7-class classification on | ||
| Representation: Thomas & Malik, 2001 | PPMI+ images. SPM: [Lazebnik et al, 2006] | ||
| Csurka et al, 2004 Fei-Fei & Perona, | DPM: [Felzenszwalb et al, 2008] | ||
| 2005 Sivic et al, 2005. Grauman & | Constellation: [Fergus et al, 2003] | ||
| Darrell, 2005 Lazebnik et al, 2006. Weber | [Niebles & Fei-Fei, 2007]. 29. | ||
| et al, 2000 Fergus et al, 2003 Leibe et | 30 | Classifying Playing vs. Not playing. | |
| al, 2004 Felzenszwalb et al, 2005 Bourdev | Seven 2-class classification problem; | ||
| & Malik, 2009. 9. | PPMI+ vs. PPMI- for each instrument. | ||
| 10 | Grouplet: our intuition. Capture the | Bassoon. Erhu. Flute. French horn. | |
| subtle difference in human-object | Saxophone. Violin. Average PPMI+ images. | ||
| interactions. Part-based configuration | Average PPMI- images. 30. | ||
| Co-occurrence Discriminative Dense. | 31 | Classifying Playing vs. Not playing. | |
| Grouplet Representation: 10. | Seven 2-class classification problem; | ||
| 11 | Outline. Intuition of Grouplet | PPMI+ vs. PPMI- for each instrument. | |
| Representation Grouplet Feature | Guitar. Average PPMI+ images. Average | ||
| Representation Using Grouplet for | PPMI- images. 31. | ||
| Recognition Dataset & Experiments | 32 | Detecting people playing musical | |
| Conclusion. 11. | instruments. Procedure: Face detection | ||
| 12 | Grouplet representation (e.g. | with a low threshold; Crop and normalize | |
| 2-Grouplet). Gaussian distribution. Visual | image regions; 8-class classification. 7 | ||
| codewords. Notations. I: Image. P: | classes of playing instruments; Another | ||
| Reference point in the image. ?: Grouplet. | class of not playing any instrument. | ||
| ?i: Feature unit. Ai: Visual codeword; xi: | Playing saxophone. No playing. No playing. | ||
| Image location; ?i: Variance of spatial | 32. | ||
| distribution. 12. | 33 | Detecting people playing musical | |
| 13 | Grouplet representation (e.g. | instruments. Area under the | |
| 2-Grouplet). Gaussian distribution. Visual | precision-recall curve: Out method: 45.7%; | ||
| codewords. Notations. I: Image. P: | Spatial pyramid: 37.3%. 33. | ||
| Reference point in the image. ?: Grouplet. | 34 | Detecting people playing musical | |
| ?i: Feature unit. ?(?,I): Matching score | instruments. Area under the | ||
| of ? and I. ?(?i,I): Matching score of ?i | precision-recall curve: Out method: 45.7%; | ||
| and I. Matching score between ? and I. | Spatial pyramid: 37.3%. False detection. | ||
| Matching score between ?i and I. Ai: | Missed detection. 34. Playing French horn. | ||
| Visual codeword; xi: Image location; ?i: | 35 | Examples of Mined Grouplets. Playing | |
| Variance of spatial distribution. 13. | bassoon: Playing saxophone: Playing | ||
| 14 | Grouplet representation (e.g. | violin: Playing guitar: 35. | |
| 2-Grouplet). Gaussian distribution. Visual | 36 | Conclusion. The Next Talk. Holistic | |
| codewords. Notations. I: Image. P: | image-based classification. Detailed | ||
| Reference point in the image. ?: Grouplet. | understanding and reasoning. Vs. Pose | ||
| ?i: Feature unit. ?(?,I): Matching score | estimation & object detection. [B. Yao | ||
| of ? and I. ?(?i,I): Matching score of ?i | and L. Fei-Fei. “Grouplet: A structured | ||
| and I. For an image patch: ?(x): Image | image representation for recognizing human | ||
| neighborhood of x. Matching score between | and object interactions.” CVPR 2010.]. [B. | ||
| ? and I. Matching score between ?i and I. | Yao and L. Fei-Fei. “Modeling mutual | ||
| Codeword assignment score. Gaussian | context of object and human pose in | ||
| density value. Ai: Visual codeword; xi: | human-object interaction activities.” CVPR | ||
| Image location; ?i: Variance of spatial | 2010.]. 36. | ||
| distribution. a?: Its visual appearance; | 37 | Thanks to. Juan Carlos Niebles, Jia | |
| x?: Its image location. 14. | Deng, Jia Li, Hao Su, Silvio Savarese, and | ||
| 15 | Grouplet representation (e.g. | anonymous reviewers. And You. 37. | |
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