Titlebook: Computer Vision –ACCV 2016; 13th Asian Conferenc Shang-Hong Lai,Vincent Lepetit,Yoichi Sato Conference proceedings 2017 Springer Internatio

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MARVEL: A Large-Scale Image Dataset for Maritime Vesselss, such as cars, bird species, and aircrafts, have been increasing. The collection of large datasets has helped vision based classification approaches and led to significant improvements in performances of the state-of-the-art methods. Visual classification of maritime vessels is another important t

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Allowance

EXCEL

R-CNN for Small Object Detectionring a small part of an image is largely ignored. As a result, the state-of-the-art object detection algorithm renders unsatisfactory performance as applied to detect small objects in images. In this paper, we dedicate an effort to bridge the gap. We first compose a benchmark dataset tailored for th

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Object-Centric Representation Learning from Unlabeled Videosf (1) intensive manual annotations and (2) an inherent restriction in the scope of data relevant for learning. In this work, we explore unsupervised feature learning from unlabeled video. We introduce a novel . approach to temporal coherence that encourages similar representations to be learned for

Fortuitous

全國性

Erratum to: Gentile da Fabriano,he fusion step optimizes over binary and continuous sets of variables representing label ranges. Further, each fusion step can optimize over additional continuous unknowns. We demonstrate the general method on a variational-inspired stereo approach, and optionally optimize over radiometric changes between the images as well.

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Conference proceedings 2017ce and Gestures; Image Alignment; Computational Photography and Image Processing; Language and Video; 3D Computer Vision; Image Attributes, Language, and Recognition; Video Understanding; and 3D Vision..

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0302-9743 tions; Faces; Computational Photography; Face and Gestures; Image Alignment; Computational Photography and Image Processing; Language and Video; 3D Computer Vision; Image Attributes, Language, and Recognition; Video Understanding; and 3D Vision..978-3-319-54192-1978-3-319-54193-8Series ISSN 0302-9743 Series E-ISSN 1611-3349