Fast R-CNN

Predicting the Future Behavior of a Time-Varying ...

Predicting the Future Behavior of a Time-Varying Probability Distribution Christoph H. Lampert IST Austria chl@ist.ac.at Abstract We study the problem of predicting the future, though

  Future, Time, Distribution, Probability, The future, Varying, A time varying probability distribution

Deep Convolutional Neural Fields for Depth Estimation From ...

volutional neural networks (CNN). CNN features have been setting new records for a wide variety of vision applica-tions [13]. Despite all the successes in classification prob-

  Network, Neural network, Neural, Convolutional, Convolutional neural

Image Style Transfer Using Convolutional Neural Networks

Image Style Transfer Using Convolutional Neural Networks Leon A. Gatys Centre for Integrative Neuroscience, University of Tubingen, Germany¨ Bernstein Center for Computational Neuroscience, Tubingen, Germany¨

  Styles, Transfer, Style transfer

Deep Residual Learning for Image Recognition

the residual learning principle is generic, and we expect that it is applicable in other vision and non-vision problems. 2. Related Work Residual Representations. In image recognition, VLAD [18] is a representation that encodes by the residual vectors with respect to a dictionary, and Fisher Vector [30] can be

  Image, Learning, Residual, Recognition, Residual learning for image recognition, Image recognition, Residual learning

NTU RGB+D: A Large Scale Dataset for 3D Human Activity ...

Multiview 3D event [43] and Northwestern-UCLA [40] datasets used more than one Kincect cameras at the same time to collect multi-view representations of the same ac-tion, and scale up the number of samples. It is worth mentioning, there are more than 40 datasets specifically for 3D human action recognition [47]. Al-

  Events, Recognition

Single-Image Crowd Counting via Multi-Column …

Single-Image Crowd Counting via Multi-Column Convolutional Neural Network Yingying Zhang Desen Zhou Siqin Chen Shenghua Gao Yi Ma Shanghaitech University {zhangyy2,zhouds,chensq,gaoshh,mayi}@shanghaitech.edu.cn Abstract ... column CNN is adaptive to (hence the overall network

  Single, Columns

Unsupervised Visual Representation Learning by Context ...

high-resolution natural images. Unsupervisedrepresentation learning can also be formu-lated as learning an embedding (i.e. a feature vector for each image) where images that are semantically similar are close, while semantically different ones are far apart. One way to build such a representation is to create a supervised

  High, Learning, Visual, Representation, Resolution, Unsupervised, Unsupervised visual representation learning by

Hierarchical Convolutional Features for Visual Tracking

Visual representations are of great importance for object tracking. Numerous hand-crafted features have been used to represent the target appear-ance such as subspace representation [24] and color his-tograms [37]. The recent years have witnessed significant

  Feature, Tracking, Visual, Representation, Hierarchical, Convolutional, Visual representation, Hierarchical convolutional features for visual tracking

Learning Spatiotemporal Features With 3D Convolutional ...

the networks lose their input’s temporal signal after the first convolution layer. Only the Slow Fusion model in [18] uses 3D convolutions and averaging pooling in its first 3convo-lution layers. We believe this is the key reason why it per-forms best …

  Network

Convolutional Neural Networks at Constrained Time Cost

Convolutional neural networks (CNNs) [15, 14] have re-cently brought in revolutions to the computer vision area. Deep CNNs not only have been continuously advancing the image classification accuracy [14, 21, 24, 1, 9, 22, 23], but also play as generic feature extractors for various recogni-tion tasks such as object detection [6, 9], semantic ...

  Network, Neural, Convolutional, Constrained, Convolutional neural networks at constrained

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

  Book, Orange, Orange book

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

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

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 …

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

  Paper