Conference Papers

Siamese Neural Networks for One-shot Image Recognition

Proceedings of the 32nd International Conference on Machine Learning, Lille, France, 2015. JMLR: W&CP volume 37. Copy-right 2015 by the author(s). Figure 1. Example of a 20-way one-shot classification task using the Omniglot dataset. The lone test image is shown above the grid of 20 images representing the possible unseen classes that we can

  Conference

Object Recognition from Local Scale-Invariant Features

Proc. of the International Conference on Computer Vision,Corfu (Sept. 1999) An object recognitionsystem hasbeen developed thatuses a newclassoflocalimagefeatures. Thefeaturesare invariant to imagescaling, translation,and rotation,and partiallyin-variant to illuminationchanges and affineor 3D projection.

  Conference

Rich Feature Hierarchies for Accurate Object Detection and ...

Region proposals. A variety of recent papers offer meth-ods for generating category-independent region proposals. Examples include: objectness [1], selective search [32], category-independent object proposals [11], constrained parametric min-cuts (CPMC) [5], multi-scale combinatorial grouping [3], and Cires¸an et al. [6], who detect mitotic cells

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Trusted Computer System Evaluation Criteria ['Orange Book']

Oct 08, 1998 · DoD 5200.28-STD Supersedes CSC-STD-00l-83, dtd l5 Aug 83 Library No. S225,7ll. DEPARTMENT OF DEFENSE STANDARD. DEPARTMENT OF. DEFENSE. TRUSTED COMPUTER. SYSTEM EVALUATION

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EAST: An Efficient and Accurate Scene Text Detector

EAST: An Efficient and Accurate Scene Text Detector Xinyu Zhou, Cong Yao, He Wen, Yuzhi Wang, Shuchang Zhou, Weiran He, and Jiajun Liang Megvii Technology Inc., Beijing, China

WEBSITE QUALITY ASSESSMENT CRITERIA

WEBSITE QUALITY ASSESSMENT CRITERIA (Research paper: IQ Concepts, Tools, Metrics, Measures and Methodologies) Vassilis S. Moustakis1,2, Charalambos Litos1, Andreas Dalivigas1, and Loukas Tsironis1 1 Department of Production and Management Engineering, Technical University of Crete, Chania 73100, Greece 2 Institute of Computer Science, Foundation for …

Fast R-CNN

Fast R-CNN Ross Girshick Microsoft Research rbg@microsoft.com Abstract This paper proposes a Fast Region-based Convolutional Network method (Fast R-CNN) for object detection. Fast R-CNN builds on previous work to efficiently classify ob-