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Computer Vision – ECCV 2016: 14th European Conference, - download pdf or read online

By Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling

ISBN-10: 3319464922

ISBN-13: 9783319464923

ISBN-10: 3319464930

ISBN-13: 9783319464930

The eight-volume set comprising LNCS volumes 9905-9912 constitutes the refereed lawsuits of the 14th eu convention on computing device imaginative and prescient, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016.
The 415 revised papers awarded have been conscientiously reviewed and chosen from 1480 submissions. The papers conceal all elements of computing device imaginative and prescient and development acceptance reminiscent of 3D laptop imaginative and prescient; computational images, sensing and exhibit; face and gesture; low-level imaginative and prescient and photo processing; movement and monitoring; optimization equipment; physics-based imaginative and prescient, photometry and shape-from-X; attractiveness: detection, categorization, indexing, matching; segmentation, grouping and form illustration; statistical tools and studying; video: occasions, actions and surveillance; purposes. they're prepared in topical sections on detection, acceptance and retrieval; scene figuring out; optimization; picture and video processing; studying; motion job and monitoring; 3D; and nine poster sessions.

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Extra info for Computer Vision – ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV

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6M, and MCAD dataset validate that our method outperforms the state-of-the-art algorithms on both 2D joints localization and 3D motion recovery. Moreover, the evaluation results on HumanEva indicates that the performance of our proposed single-view approach is comparable to that of the multiview deep learning counterpart. Keywords: Human pose estimation 1 · Height-map Introduction Marker-less motion capture is an active field of research in computer vision and graphics with applications in computer animation, video surveillance, biomedical research, and sports science.

Knowl. Eng. Rev. 17(02), 107–127 (2002) 23. : Attribute-based classification for zeroshot visual object categorization. In: TPAMI (2013) 24. : Explainable artificial intelligence for training and tutoring. Technical report, DTIC Document (2005) 25. : Explaining robot actions. In: Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction. ACM (2012) 26. : Generation and comprehension of unambiguous object descriptions. In: CVPR (2016) 27. : Learning like a child: fast novel visual concept learning from sentence descriptions of images.

We apply our metrics to instances in which the compact bilinear classifier predicts the correct label as is unclear if the best explanatory text should be more similar to correct or predicted classes. However, the same trends hold if we apply our metrics to all generated sentences. 5 Results We demonstrate that our model generates superior visual explanations and produces image and class relevant text. Additionally, generating visual explanations results in higher quality sentences based on common sentence generation metrics.

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Computer Vision – ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV by Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling

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