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
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Predicting the Future Behavior of a Time-Varying ...
www.cv-foundation.orgPredicting 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
Deep Convolutional Neural Fields for Depth Estimation From ...
www.cv-foundation.orgvolutional 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-
Image Style Transfer Using Convolutional Neural Networks
www.cv-foundation.orgImage Style Transfer Using Convolutional Neural Networks Leon A. Gatys Centre for Integrative Neuroscience, University of Tubingen, Germany¨ Bernstein Center for Computational Neuroscience, Tubingen, Germany¨
Deep Residual Learning for Image Recognition
www.cv-foundation.orgthe 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
NTU RGB+D: A Large Scale Dataset for 3D Human Activity ...
www.cv-foundation.orgMultiview 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-
Single-Image Crowd Counting via Multi-Column …
www.cv-foundation.orgSingle-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
Hierarchical Convolutional Features for Visual Tracking
www.cv-foundation.orgVisual 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
Learning Spatiotemporal Features With 3D Convolutional ...
www.cv-foundation.orgthe 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 …
Convolutional Neural Networks at Constrained Time Cost
www.cv-foundation.orgConvolutional 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 ...
Fully Convolutional Networks for Semantic Segmentation
www.cv-foundation.orgConvolutional networks are powerful visual models that yield hierarchies of features. We show that convolu-tional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segmen-tation. Our key insight is to build “fully convolutional” networks that take input of arbitrary size and produce
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Deep Bilateral Learning for Real-Time Image Enhancement
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MagFace: A Universal Representation for Face Recognition ...
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Deep High-Resolution Representation Learning for Human ...
openaccess.thecvf.comDeep High-Resolution Representation Learning for Human Pose Estimation Ke Sun1,2∗† Bin Xiao2∗ Dong Liu1 Jingdong Wang2‡ 1University of Science and Technology of China 2Microsoft Research Asia sunk@mail.ustc.edu.cn, dongleiu@ustc.edu.cn, {Bin.Xiao,jingdw}@microsoft.com Abstract In this paper, we are interested in the human pose es-
Dual Super-Resolution Learning for Semantic Segmentation
openaccess.thecvf.comDual Super-Resolution Learning for Semantic Segmentation Li Wang1, ∗, Dong Li1, Yousong Zhu2, Lu Tian1, Yi Shan1 1 Xilinx Inc., Beijing, China. 2 Institute of Automation, Chinese Academy of Sciences, Beijing, China. {liwa, dongl, lutian, yishan}@xilinx.com, yousong.zhu@nlpr.ia.ac.cn Abstract Current state-of-the-art semantic segmentation method-
Knowledge Representation and Reasoning
web.stanford.edu– Knowledge Representation & Reasoning by Brachman & Levesque (available online) • Lectures – Tuesday and Thursday, 12:50-2:05, 300-300 • Grades – Four Assignments (40%), Mid-term (25%), Final (35%) • Prerequisites – First order logic and Resolution (at the level of CS157) • There will be two tutorial sections to cover this material
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www.deeplearningbook.orghuman time and e ffort; it can take decades for an entire community of researchers. The quintessential example of a representation learning algorithm is the au-toencoder. An autoencoder is the combination of an encoder function that converts the input data into a different representation, and a decoder function
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www.tutorialspoint.comlearning algorithms for language processing. Study of Human Languages Language is a crucial component for human lives and also the most fundamental aspect of our behavior. We can experience it in mainly two forms – written and spoken. In the written form, it is a way to pass our knowledge from one generation to the next. In the