Example: confidence
Going Deeper With Convolutions - cv-foundation.org
3. Motivation and High Level Considerations The most straightforward way of improving the perfor-mance of deep neural networks is by increasing their size. This includes both increasing the depth – the number of net-Figure 1: Two distinct classes from the 1000 classes of the ILSVRC 2014 classification challenge. Domain knowledge is re-
Tags:
Information
Domain:
Source:
Link to this page:
Documents from same domain
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¨
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 ...
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-
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
Unsupervised Visual Representation Learning by Context ...
www.cv-foundation.orghigh-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
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 …
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 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-
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
Related documents
Support-vector networks - Springer
link.springer.comWith this extension we consider the support-vector networks as a new class of learning machine, as powerful and universal as neural networks. In Section 5 we will demonstrate how well it generalizes for high degree polynomial decision surfaces (up to order 7) in a high dimensional space (dimension 256).
Frequency Principle: Fourier Analysis Sheds Light on Deep ...
ins.sjtu.edu.cnWe study the training process of Deep Neural Networks (DNNs) from the Fourier analysis perspective. We demonstrate a very universal Frequency Principle (F-Principle) — DNNs often fit target functions from low to high frequencies — on high-dimensional benchmark datasets such as MNIST/CIFAR10 and deep neural net-works such as VGG16.
Self-Supervised Learning
cs229.stanford.edu•Goal: represent words as vectors for input into neural networks. •One-hot vectors? (single 1, rest 0s) pizza = [0 0 0 0 0 1 0 … 0 0 0 0 0 ] pie = [0 0 0 0 0 0 0 … 0 0 0 1 0 ] ☹Millions of words high-dimensional, sparse vectors ☹No notion of word similarity •Instead: we want a dense, low-dimensional vector for each word such that ...
![arXiv:1512.00567v3 [cs.CV] 11 Dec 2015](/cache/preview/e/9/b/2/7/3/a/d/thumb-e9b273ad52b2edb6f94a58f5caa30476.jpg)
arXiv:1512.00567v3 [cs.CV] 11 Dec 2015
arxiv.orgcused on finding higher performing convolutional neural networks. Starting in 2014, the quality of network architec-tures significantly improved by utilizing deeper and wider networks. VGGNet [18] and GoogLeNet [20] yielded simi-larly high performance in the 2014 ILSVRC [16] classifica-tion challenge. One interesting observation was that gains
![Abstract arXiv:1611.05431v2 [cs.CV] 11 Apr 2017](/cache/preview/b/8/4/6/e/f/c/6/thumb-b846efc624a92f9b5a173084e67ead39.jpg)
Abstract arXiv:1611.05431v2 [cs.CV] 11 Apr 2017
arxiv.orgparameters, and depth is exposed as an essential dimension in neural networks. Moreover, we argue that the simplicity of this rule may reduce the risk of over-adapting the hyper-parameters to a specific dataset. The robustness of VGG-nets and ResNets has been proven by various visual recog-nition tasks [7,10,9,28,31,14] and by non-visual tasks
Andrew G. Howard Menglong Zhu Bo Chen Dmitry ... - arXiv
arxiv.orgcient neural networks in the recent literature, e.g. [16,34, 12,36,22]. Many different approaches can be generally categorized into either compressing pretrained networks or training small networks directly. This paper proposes a class of network architectures that allows a model devel-oper to specifically choose a small network that matches
Selective Kernel Networks - CVF Open Access
openaccess.thecvf.comSelective Kernel Networks Xiang Li∗1,2, Wenhai Wang†3,2, Xiaolin Hu‡4 and Jian Yang§1 1PCALab, Nanjing University of Science and Technology 2Momenta 3Nanjing University 4Tsinghua University Abstract In standard Convolutional Neural Networks (CNNs), the receptive fields of artificial neurons in each layer are de-
ECA-Net: Efficient Channel Attention for Deep ...
openaccess.thecvf.comDeep convolutional neural networks (CNNs) have been widely used in computer vision community, and have ∗Qinghua Hu is the corresponding author. Email: {qlwang, wubanggu, huqinghua}@tju.edu.cn. The work was sup-ported by the National Natural Science Foundation of China (Grant No. 61806140, 61876127, 61925602, 61971086, U19A2073, 61732011), Ma-