In particular, our heatmapping procedure was applied to this network without any further training or retraining. Related Work. Abstract: In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. VGG-16 pre-trained model for Keras. The features are invariant to image scaleand rotation, and are shown to provide robust matching across a a substantial range of affine dis-. We found that on average each image had descriptions from 17 different clusters. The experiments are conducted on large-scale ImageNet-1K and Places365 datasets, and the results demonstrate our 3G-Net outperforms its counterparts while achieving very competitive performance to state-of-the-arts. 2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM. 2 million images in total. Please cite it when reporting ILSVRC2012 results or using the dataset. Edward Pepperell, Peter Corke, Michael Milford: IJRR2015 CBD and Highway Datasets. This paper investigates the performance of the pre-trained CNN with multi-class support vector machine (SVM) classifier and the performance of transfer learning using the AlexNet model to perform. Abstract: Large Convolutional Network models have recently demonstrated impressive classification performance on the ImageNet benchmark. News of August 6, 2017: This paper of 2015 just got the first Best Paper Award ever issued by the journal Neural Networks, founded in 1988. Napol Siripibal , Siriporn Supratid , Chaitawatch Sudprasert, A Comparative Study of Object Recognition Techniques: Softmax, Linear and Quadratic Discriminant Analysis Based on Convolutional Neural Network Feature Extraction, Proceedings of the 2019 International Conference on Management Science and Industrial Engineering, May 24-26, 2019. For details, refer to paper. ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky *, Jia Deng *, Hao Su , Jonathan Krause , Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy , Aditya Khosla , Michael Bernstein , Alexander Berg and Li Fei-Fei. and Caetano, T. 2 from the paper, you may want to train BENN multiple times and pick the best combination. The paper also used Dropout in the last two layers. Julian McAuley Associate Professor. Berg and Li Fei-Fei. Comparison experiments of different pooling methods are performed on three widely used datasets: LFW, CIFAR-10, and ImageNet. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM. 2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. 2 million images in total. Sutskever , and G. ImageNet LSVRC 2012 Validation Set (Object Detection) Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. You can't find a book this detailed in any other. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. Using the pre-trained model is easy; just start from the example code included in the [quickstart guide](quick. * Paper also included sample images generated by conditioning on human portraits and by training a PixelCNN auto-encoder on ImageNet patches. The paper talks about techniques to save memory bandwidth, networking bandwidth, and engineer bandwidth for efficient deep learning. I have also been recently awarded an IBM Faculty Award and a Google Faculty Research Award. bibtex custom thesis paper. In this way, 3GNet can be flexibly trained in an end-to-end manner. 2 million images in total. In this paper we propose to automatically populate ImageNet with many more bounding-boxes, by leveraging existing manual annotations. Room 4102 Computer Science Department @ UCSD. Abstract: In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. We demonstrate experimentally that it outperforms purely visual distances. We trained a large, deep convolutional neural network to classify the 1. This paper presents a new state-of-the-art for document image classification and retrieval, using features learned by deep convolutional neural networks (CNNs). Neural Information Processing Systems (NIPS). Detect to Track and Track to Detect C. NVIDIA has released the Titan Xp which is an update to the Titan X Pascal (they both use the Pascal GPU core). Sutskever , and G. September 2, 2014: A new paper which describes the collection of the ImageNet Large Scale Visual Recognition Challenge dataset, analyzes the results of the past five years of the challenge, and even compares current computer accuracy with human accuracy is now available. Request PDF on ResearchGate | ImageNet Training in Minutes | In this paper, we investigate large scale computers' capability of speeding up deep neural networks (DNN) training. The paper talks about techniques to save memory bandwidth, networking bandwidth, and engineer bandwidth for efficient deep learning. We present a highly accurate single-image super-resolution (SR) method. This paper describes the TensorFlow interface and an implementation of that interface that we have built at Google. Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, Kaiming He arXiv preprint 2017 / bibtex. Constructing such a large-scale database is a challenging task. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification, Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. Imagenet classification with deep convolutional neural networks A. The purpose of this paper is three-fold. The purpose of this study was to evaluate the performance of the TOPCON SP-2000P and IMAGEnet system in terms of (a) the difference in results between automated endothelial cell analysis and retraced cell analysis, (b) the differences in the endothelial cell analysis when. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation. Visit our research group homepage Polo Club of Data Science at Georgia Tech for more related research!. Accurate segmentation of anatomical structures in chest radiographs is essential for many computer-aided diagnosis tasks. Comparable problems such as object detection in images have reaped enormous benefits from comprehensive datasets -- principally ImageNet. In particular, our heatmapping procedure was applied to this network without any further training or retraining. Berg and Li Fei-Fei. We propose a fully computational approach for modeling the structure in the space of visual tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Imagenet classification with deep convolutional neural networks A. Detect to Track and Track to Detect C. Network Dissection is a framework for quantifying the interpretability of latent representations of CNNs by evaluating the alignment between individual hidden units and a set of semantic concepts. A paper that describes the dataset and presents performance indexing has been presented at the Multimedia Commons 2016 Workshop in Amsterdam during ACM Multimedia 2016: YFCC100M HybridNet fc6 Deep Features for Content-Based Image Retrieval Giuseppe Amato, Fabrizio Falchi, Claudio Gennaro, and Fausto Rabitti. The implementation of the resulting binary CNN, denoted as ABC-Net, is shown to achieve much closer performance to its full-precision counterpart, and even reach the comparable prediction accuracy on ImageNet and forest trail datasets, given adequate binary weight bases and activations. Specifically, given a natural sentence and a video, we localize a spatio-temporal tube in the video that semantically corresponds to the given sentence, with no reliance on any spatio-temporal annotations during training. ImageNet Classification with Deep Convolutional Neural Networks Part of: Advances in Neural Information Processing Systems 25 (NIPS 2012) [PDF] [BibTeX] [Supplemental]. ReLU was a key aspect which was not so often used before. 2 million images in total. All training images are collected from the ImageNet DET training/val sets [1], while test images are collected from the ImageNet DET test set and the SUN data set [2]. Student paper assignments. Review This is an early work of using deep CNN. Paper / Bibtex. 1 of the paper. The one that started it all (Though some may say that Yann LeCun’s paper in 1998 was the real pioneering publication). Abstract; Modern convolutional networks are not shift-invariant, as small input shifts or translations can cause drastic changes in the output. The insights gained from our analysis enable building a novel distance function between images assessing whether they are from the same basic-level category. See who you know at Imagenet LLC, leverage your professional network, and get hired. [ Paper] [ BibTex] [ Poster] [ Code] [ Journal Version] Proposing a segmental architecture and obtaining the state-of-the-art performance on UCF101 and HMDB51 CUHK & ETHZ & SIAT Submission to ActivityNet Challenge 2016. Commonly used downsampling methods, such as max-pooling, strided-convolution, and average-pooling, ignore the sampling theorem. In this paper, we introduce optimization methods which we applied to this challenge. The pretrained MobileNetV2 1. Make sure to either explain your thinking and reasoning or use specific examples to illustrate your points. Matej Kristan, Ales Leonardis, Jiri Matas, Michael Felsberg, Roman Pflugfelder, Luka Cehovin Zajc, Tomas Vojir, Gustav Häger, Alan Lukezic, Abdelrahman Eldesokey. This paper presents a new state-of-the-art for document image classification and retrieval, using features learned by deep convolutional neural networks (CNNs). Related Work. We find increasing our network depth shows a significant improvement in accuracy. The dblp computer science bibliography is the on-line reference for open bibliographic information on computer science journals and proceedings. In this paper we investigate the latest fully-convolutional architectures for the task of multi-class segmentation of the lungs field, heart and clavicles in a chest radiograph. Welcome to join us and visit Stanford!. The mean DNN confidence scores for these images is 99. After the competition, we further improved our models, which has lead to the following ImageNet classification results. Whereas traditional convolutional networks with L layers have L connections—one between each layer and its subsequent. Berg and Li Fei-Fei. Use the following template to cite a book using the BibTeX generic citation style citation style. paper | bibtex When using the Places2 dataset for the taster scene classification challenge, please cite: Bolei Zhou, Aditya Khosla, Agata Lapedriza, Antonio Torralba and Aude Oliva. [email protected] This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching betweendifferent views of an object or scene. Challenge 2. Automated pavement distress detection and classification has remained one of the high-priority research areas for transportation agencies. In contrast, the performance of defense techniques still lags behind. Abstract: We propose a deep convolutional neural network architecture codenamed "Inception", which was responsible for setting the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC 2014). Optimization of robust loss functions for weakly-labeled image taxonomies: an imagenet case study. 2 million images in total. and Dong, W. In this paper we address both issues. For a more efficient implementation for GPU, head over to here. The main hallmark of this architecture is the improved utilization of the computing. IJCV 2015 Learning Features and Parts for Fine-Grained Recognition. We find increasing our network depth shows a significant improvement in accuracy. net) ICCV Slides (pptx, 8MB) Citation Carl Doersch, Abhinav Gupta, and Alexei A. UPDATE: to support our CVPR2015 paper on “Toward Open World”, we wanted to compare with 1-vs-set, but the libSVM-onevset version was too slow. Welcome to join us and visit Stanford!. By using feature inversion to visualize millions of activations from an image classification network, we create an explorable activation atlas of features the network has learned which can reveal how the network typically represents some concepts. * In the case of conditioning on ImageNet classes, the log likelihood measure did not improve a lot but the visual quality of the generated sampled was significantly improved. Berg, Li Fei-Fei. The results are no worse than their ImageNet pre-training counterparts even when using the hyper-parameters of the baseline system (Mask R-CNN) that were optimized for fine-tuning pre-trained models, with the sole exception of increasing the number of training iterations so the randomly initialized models may converge. [pdf] [bibtex] Software Clone the source from github: libSVM-onevset. Due to overfitting and optimization instability as observed in Section 6. [ Paper] [ BibTex] [ Poster] [ Code] [ Journal Version] Proposing a segmental architecture and obtaining the state-of-the-art performance on UCF101 and HMDB51 CUHK & ETHZ & SIAT Submission to ActivityNet Challenge 2016. The one that started it all (Though some may say that Yann LeCun’s paper in 1998 was the real pioneering publication). Constructing such a large-scale database is a challenging task. Tsung-Yu Lin, Aruni RoyChowdhury and Subhransu Maji International Conference on Computer Vision (ICCV), 2015 pdf, pdf-supp, bibtex, code. This is the code repository for the KDD 2018 Applied Data Science paper: SHIELD: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression. This paper presents a new state-of-the-art for document image classification and retrieval, using features learned by deep convolutional neural networks (CNNs). This desirable property is demonstrated in this paper by the heatmapping of images classified by the third-party GPU-trained ImageNet neural network. A key challenge is how to achieve efficiency in both feature extraction and classifier training without compromising performance. Mcauley and Arnau Ramisa and Tibério S. Results Accuracy on various fine-grained recognition datasets are below. Here, we show the ImageNet categories for which our colorization helps and hurts the most on object classification. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3. For references, we also list the performance comparison of Kinetics and ImageNet pretrained models on two action understanding tasks, i. The insights gained from our analysis enable building a novel distance function between images assessing whether they are from the same basic-level category. The ones marked * may be different from the article in the profile. Imagenet classification with deep convolutional neural networks A. @incollection{NIPS2012_4824, title = {ImageNet Classification with Deep Convolutional Neural Networks}, author = {Alex Krizhevsky and Sutskever, Ilya and Hinton. ImageNet test set, and won the 1st place in the ILSVRC 2015 classification competition. Illustration of NetAdapt. Dataset: Olga Russakovsky*, Jia Deng*, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. This paper aims to develop a method that is able to select for an input image the best salient object detection result from many results produced by different methods. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep. Join LinkedIn today for free. Switchable Whitening unifies various whitening and standardization techniques in a general form, and adaptively learns their importance ratios for different tasks. ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky *, Jia Deng *, Hao Su , Jonathan Krause , Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy , Aditya Khosla , Michael Bernstein , Alexander Berg and Li Fei-Fei. and Fei-Fei, L. Due to overfitting and optimization instability as observed in Section 6. Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval. 2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. without the words. Introduction. We contribute a large scale database for 3D object recognition, named ObjectNet3D, that consists of 100 categories, 90,127 images, 201,888 objects in these images and 44,147 3D shapes. Advances in neural information processing systems , page 1097--1105. "Distilling the Knowledge in a Neural Network", in NIPS Deep Learning Workshop 2014. These ICCV 2015 papers are the Open Access versions, provided by the Computer Vision Foundation. Abstract: Synchronized stochastic gradient descent (SGD) optimizers with data parallelism are widely used in training large-scale deep neural networks. and Socher, R. A 5-min video summary of the paper. In this paper we explore both issues. I am serving the Publication Chair for International Conference on 3DVision (3DV-16), Stanford, USA. @incollection{NIPS2012_4824, title = {ImageNet Classification with Deep Convolutional Neural Networks}, author = {Alex Krizhevsky and Sutskever, Ilya and Hinton. Please sign up to review new features, functionality and page designs. Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval. Deep Learning in Neural Networks: An Overview. Generative adversarial networks has been sometimes confused with the related concept of “adversar-ial examples” [28]. 3% less top-1 accuracy on CIFAR-10 and 0. Abstract: Large Convolutional Network models have recently demonstrated impressive classification performance on the ImageNet benchmark. the ImageNet dataset. This communication step usually becomes the bottleneck of the system when the number of GPUs becomes large. This paper describes the creation of Audio Set, a large-scale dataset of manually-annotated audio events that endeavors to bridge the gap in data availability between image and audio research. The first group of tests are linear classifiers for semantic classification on each layer in the network. Use the following template to cite a book using the BibTeX generic citation style citation style. 摘要: Abstract: We propose a deep convolutional neural network architecture codenamed "Inception", which was responsible for setting the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC 2014). Our approach leverages recent advances in deep networks, exploiting both low-level and semantic representations during colorization. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3. For more background on VOC, the following journal paper discusses some of the choices we made and our experience in running the challenge, and gives a more in depth discussion of the 2007 methods and results: The PASCAL Visual Object Classes (VOC) Challenge. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. [1] Cichy RM, Khosla A, Pantazis D, Torralba A, and Oliva A. Exploring Neural Networks with Activation Atlases. }, TITLE = {{ImageNet: A Large-Scale. Random Erasing Data Augmentation Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, Yi Yang Arxiv, 2017 abstract / bibtex / PDF / Torchvision / CIFAR / ImageNet / Re-ID. Lewis Smith, Yarin Gal UAI, 2018 [Paper] Using Pre-trained Full-Precision Models to Speed Up Training Binary Networks For Mobile Devices Binary Neural Networks (BNNs) are well-suited for deploying Deep Neural Networks (DNNs) to small embedded devices but state-of-the-art BNNs need to be trained from scratch. 1% less on ImageNet whilst reducing the search time from 36 hours down to 1 hour. MobileNetV2 with a spectrum of width multipliers. Generative adversarial networks has been sometimes confused with the related concept of "adversar-ial examples" [28]. This figure shows several t-SNE feature visualizations on the ILSVRC-2012 validation set. In this paper we address both issues. Visit our research group homepage Polo Club of Data Science at Georgia Tech for more related research!. of Electronic Engineering , the Chinese University of Hong Kong. This paper, titled “ImageNet Classification with Deep Convolutional Networks”, has been cited a total of 6,184 times and is widely regarded as one of the most influential publications in the field. For a more efficient implementation for GPU, head over to here. Some groups sizes are set to 2 due to the memory limitation of GPU at that time. [ Paper] [ BibTex] [ Poster] [ Code] [ Journal Version] Proposing a segmental architecture and obtaining the state-of-the-art performance on UCF101 and HMDB51 CUHK & ETHZ & SIAT Submission to ActivityNet Challenge 2016. The results of the 2014 ImageNet Large Scale Visual Recognition Challenge (ILSVRC) were published a few days ago. Welcome to join us and visit Stanford!. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. Sergio Guadarrama, Erik Rodner, Kate Saenko, Ning Zhang, Ryan Farrell, Jeff Donahue and Trevor Darrell. Paper / ArXiv / Code & Model / Bibtex: Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks Jie Hu*, Li Shen*, Samuel Albanie*, Gang Sun, and Andrea Vedaldi Advances in Neural Information Processing Systems (NeurIPS), 2018. "Switchable Whitening for Deep Representation Learning", ICCV2019. IEEE International Conference on Computer Vision (ICCV), 2015. In this paper, we describe a framework that simultaneously utilizes shared representation, reconstruction sparsity, and parallelism to enable real-time multiclass object detection with deformable part models at 5Hz on a laptop computer with almost no decrease in task performance. 2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. Liyao Gao , Hongshan Li , Zheying Lu , Guang Lin, Rotation-equivariant convolutional neural network ensembles in image processing, Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers, September 09-13, 2019, London. We clustered all of our region descriptions (read the paper for more details) into semantic clusters. We demonstrate experimentally that it outperforms purely visual distances. Adam is significantly more efficient and scalable than was previously thought possible and used 30x fewer machines to train a large 2 billion connection model to 2x higher accuracy in comparable time on the ImageNet 22,000 category image classification task than the system that previously held the record for this benchmark. the ImageNet dataset. Please sign up to review new features, functionality and page designs. You can't find a book this detailed in any other. Sydney, Australia. Make sure to either explain your thinking and reasoning or use specific examples to illustrate your points. 1 of the paper. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. In BibTeX format. There is a natural correlation between the visual and auditive elements of a video. Berg and Li Fei-Fei. Results Accuracy on various fine-grained recognition datasets are below. Surprisingly, these models do not show a. It was the first break-through in the ImageNet classification challenge (LSVRC-2010, 1000 classes). We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Not only does it cover the theory behind deep learning, it also details the implementation as well. 04910, June 2019 | This is an extended version of PrGAN 3DV 2017. However, note that frames from same clip are very correlated. Surprisingly, these models do not show a. @incollection{NIPS2012_4824, title = {ImageNet Classification with Deep Convolutional Neural Networks}, author = {Alex Krizhevsky and Sutskever, Ilya and Hinton. I'm writing a paper in latex and I have some tables and image files that are too big to fit on the page, so I wanted to just include them as extra files with the paper and reference them through the bibliography (I'm using BibTex). Paper is currently in press and expected to be published in 2016. We argue that it is time to take a step back and to analyze the status quo of the area. CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We trained a large, deep convolutional neural network to classify the 1. Advances in neural information processing systems , page 1097--1105. ILSVRC is one of the largest challenges in Computer Vision and every year teams compete to claim the state-of-the-art. ImageNet classification with Python and Keras. Listen to this podcast to learn some of the reasons why!. The features are invariant to image scaleand rotation, and are shown to provide robust matching across a a substantial range of affine dis-. Berg, Li Fei-Fei. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. Back to Yann's Home Publications LeNet-5 Demos. It was the first break-through in the ImageNet classification challenge (LSVRC-2010, 1000 classes). Advances in Neural Information Processing Systems 25 , Curran Associates, Inc. Feichtenhofer, A. Energy Minimazation Methods in Computer Vision and Pattern Recognition, 2011. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3. Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval. On a 10x MACs reduction budget, transfer learned from ImageNet to Stanford Dogs 120, AFDS achieves an accuracy that is 12. Taskonomy: Disentangling Task Transfer Learning, CVPR 2018 (Best Paper). The features are invariant to image scaleand rotation, and are shown to provide robust matching across a a substantial range of affine dis-. ImageNet LSVRC 2012 Training Set (Object Detection) Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. The very deep ConvNets were the basis of our ImageNet ILSVRC-2014 submission, where our team (VGG) secured the first and the second places in the localisation and classification tasks respectively. BibTeX @inproceedings{binyang16craft, title={Craft Objects from Images}, author={Yang, Bin and Yan, Junjie and Lei, Zhen and Li, Stan}, booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, year={2016} } This page was generated by GitHub Pages using the Cayman theme by Jason Long. In this paper we study entrylevel categories at a large scale and learn the first models for predicting entry-level categories for images. See Table 2 in the PAMI paper for a detailed comparison. Paper / ArXiv / Code & Model / Bibtex: Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks Jie Hu*, Li Shen*, Samuel Albanie*, Gang Sun, and Andrea Vedaldi Advances in Neural Information Processing Systems (NeurIPS), 2018. and Socher, R. We have successfully used our system to train a deep network 30x larger than previously reported in the literature, and achieves state-of-the-art performance on ImageNet, a visual object recognition task with 16 million images and 21k categories. In its completion, we hope ImageNet will offer tens of millions of cleanly sorted images for most of the concepts in the WordNet hierarchy. Downpour SGD and Sandblaster L-BFGS both increase the scale and speed of deep network training. BibTeX @inproceedings{binyang16craft, title={Craft Objects from Images}, author={Yang, Bin and Yan, Junjie and Lei, Zhen and Li, Stan}, booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, year={2016} } This page was generated by GitHub Pages using the Cayman theme by Jason Long. 2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. Is there a "proper" way to do this? How would an entry in the. Xception: Deep Learning with Depthwise Separable Convolutions Franc¸ois Chollet Google, Inc. Visit the post for more. We propose a unified approach to tackle the problem of object detection in realistic video. These are some additional publications directly related to collecting the challenge dataset and evaluating the results. The one that started it all (Though some may say that Yann LeCun’s paper in 1998 was the real pioneering publication). "Distilling the Knowledge in a Neural Network", in NIPS Deep Learning Workshop 2014. [ paper] [ data] [ code ] [ bibtex] ImageNet: A Large-Scale Hierarchical Image Database. Entry level categories - the labels people will use to name an object - were originally defined and studied by psychologists in the 1980s. Paper / Bibtex. September 2, 2014: A new paper which describes the collection of the ImageNet Large Scale Visual Recognition Challenge dataset, analyzes the results of the past five years of the challenge, and even compares current computer accuracy with human accuracy is now available. Comparison experiments of different pooling methods are performed on three widely used datasets: LFW, CIFAR-10, and ImageNet. We find increasing our network depth shows a significant improvement in accuracy. Stanford, UC Berkeley. The ones marked * may be different from the article in the profile. Adam is significantly more efficient and scalable than was previously thought possible and used 30x fewer machines to train a large 2 billion connection model to 2x higher accuracy in comparable time on the ImageNet 22,000 category image classification task than the system that previously held the record for this benchmark. Random Erasing Data Augmentation Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, Yi Yang Arxiv, 2017 abstract / bibtex / PDF / Torchvision / CIFAR / ImageNet / Re-ID. [email protected] New: Amazon 2018 dataset We've put together a new version of our Amazon data, including more reviews and aditional metadata. and Socher, R. This paper aims to develop a method that is able to select for an input image the best salient object detection result from many results produced by different methods. ImageNet dataset [8], the test accuracy of ResNet-50 drops [22, 7]. [ paper] [ supplementary materials] [ bibtex] What does classifying more than 10,000 image categories tell us? Jia Deng, Alex Berg, Kai Li, Li Fei-Fei European Conference on Computer Vision(ECCV), 2010. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning. The very deep ConvNets were the basis of our ImageNet ILSVRC-2014 submission, where our team (VGG) secured the first and the second places in the localisation and classification tasks respectively. We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC2014). An implicit hypothesis in modern computer vision research is that models that perform better on ImageNet necessarily perform better on other vision tasks. [email protected] 2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. ImageNet Classification with Deep Convolutional Neural Networks Part of: Advances in Neural Information Processing Systems 25 (NIPS 2012) [PDF] [BibTeX] [Supplemental]. "Distilling the Knowledge in a Neural Network", in NIPS Deep Learning Workshop 2014. Energy Minimazation Methods in Computer Vision and Pattern Recognition, 2011. The main hallmark of this architecture is the improved utilization of the computing. Sutskever , and G. Due to overfitting and optimization instability as observed in Section 6. In this paper, we present an alternative method to increase the depth. Sutskever , and G. In ICCV 2015. Compared with the existing datasets, GCC is a more large-scale crowd counting dataset in both the number of images and the number of persons. We wrote a. See Table 2 in the PAMI paper for a detailed comparison. Dataset features: Coverage of 810 km² (405 km² for training and 405 km² for testing) Aerial orthorectified color imagery with a spatial resolution of 0. This paper, titled “ImageNet Classification with Deep Convolutional Networks”, has been cited a total of 6,184 times and is widely regarded as one of the most influential publications in the field. By analogy with auto-encoders, we propose Context Encoders -- a convolutional neural network trained to generate the contents of an arbitrary image region conditioned on its surroundings. Depth is one of the keys that make neural networks succeed in the task of large-scale image recognition. Specifically, given a natural sentence and a video, we localize a spatio-temporal tube in the video that semantically corresponds to the given sentence, with no reliance on any spatio-temporal annotations during training. [pdf] [bibtex] Software Clone the source from github: libSVM-onevset. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3. In this paper, we introduce optimization methods which we applied to this challenge. First you can grab some Bibtex references from Google Scholar and throw them in a paper. For references, we also list the performance comparison of Kinetics and ImageNet pretrained models on two action understanding tasks, i. Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, Kaiming He arXiv preprint 2017 / bibtex. Although using larger mini-batch sizes can improve the system scalability by reducing the communication-to-computation ratio, it may hurt the generalization ability of the models. Bibtex Masters Thesis. 192% top-1 accuracy and 90. Abstract: In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. and Ramisa, A. Sutskever , and G. This requires the use of standard Google Analytics cookies, as well as a cookie to record your response to this confirmation request. Paper / ArXiv / Code & Model / Bibtex: Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks Jie Hu*, Li Shen*, Samuel Albanie*, Gang Sun, and Andrea Vedaldi Advances in Neural Information Processing Systems (NeurIPS), 2018. Most remarkably, we obtain Top5-Errors of only 7. The purpose of this paper is three-fold. Adversarial examples are examples found by using gradient-based optimization directly on the input to a classification network, in order to find examples that are similar to the data yet misclassified. This paper describes the TensorFlow interface and an implementation of that interface that we have built at Google. The results of the 2014 ImageNet Large Scale Visual Recognition Challenge (ILSVRC) were published a few days ago. We sampled 5-10 frames per shot from each video to create our dataset of 1. The final algorithm is fast and accurate: within 4 seconds it can generate 2,134 boxes with an Average Best Pascal Overlap score of 0. (weeks) and so we developed a multi-class extension to liblinear. 1% less on ImageNet whilst reducing the search time from 36 hours down to 1 hour. The ones marked * may be different from the article in the profile. The images were collected from the web and labeled by human labelers using Amazon's Mechanical Turk crowd-sourcing tool. ImageNet Classification with Deep Convolutional Neural Networks A. %0 Conference Paper %T Conditional Image Synthesis with Auxiliary Classifier GANs %A Augustus Odena %A Christopher Olah %A Jonathon Shlens %B Proceedings of the 34th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2017 %E Doina Precup %E Yee Whye Teh %F pmlr-v70-odena17a %I PMLR %J Proceedings of Machine Learning Research %P 2642--2651 %U http. Challenge 2. We are committed to producing original and custom papers that. ImageNet dataset [8], the test accuracy of ResNet-50 drops [22, 7]. 通过文献互助平台发起求助,成功后即可免费获取论文全文。. In this paper we address both issues. The dblp computer science bibliography is the on-line reference for open bibliographic information on computer science journals and proceedings. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Code Paper BibTex ImageNet Results Slides Press Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus, Yann LeCun @ ICLR 2014 This model obtained 1st place in the 2013 ImageNet object localization challenge. and Socher, R. Inspired by the human visual system, we explore whether low-level motion-based grouping cues can be used to. In this way, 3GNet can be flexibly trained in an end-to-end manner. Source code and models to reproduce the experiments in the paper is made publicly available. In this paper, we describe a framework that simultaneously utilizes shared representation, reconstruction sparsity, and parallelism to enable real-time multiclass object detection with deformable part models at 5Hz on a laptop computer with almost no decrease in task performance. Some groups sizes are set to 2 due to the memory limitation of GPU at that time. and Fei-Fei, L. September 2, 2014: A new paper which describes the collection of the ImageNet Large Scale Visual Recognition Challenge dataset, analyzes the results of the past five years of the challenge, and even compares current computer accuracy with human accuracy is now available.