Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
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TLDR
Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).Abstract:
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 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.read more
Citations
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Journal ArticleDOI
Learning Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection
TL;DR: This work builds up the existing state-of-the-art object detection systems and proposes a simple but effective method to train rotation-invariant and Fisher discriminative CNN models to further boost object detection performance.
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Deep Layer Aggregation
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Proceedings ArticleDOI
RGB-Infrared Cross-Modality Person Re-identification
TL;DR: The experiments show that RGB-IR cross-modality matching is very challenging but still feasible using the proposed model with deep zero-padding, giving the best performance.
Journal ArticleDOI
VideoLSTM convolves, attends and flows for action recognition
Zhenyang Li,Kirill Gavrilyuk,Efstratios Gavves,Mihir Jain,Mihir Jain,Cees G. M. Snoek,Cees G. M. Snoek +6 more
TL;DR: In this paper, the authors adapt the LSTM architecture to fit the requirements of the video medium and propose a motion-based attention mechanism for action classification, which can be used for action class label and temporal attention smoothing.
Book ChapterDOI
PlaNet - Photo Geolocation with Convolutional Neural Networks
TL;DR: This work subdividing the surface of the earth into thousands of multi-scale geographic cells, and train a deep network using millions of geotagged images, and shows that the resulting model, called PlaNet, outperforms previous approaches and even attains superhuman accuracy in some cases.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
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Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Journal ArticleDOI
Regression Shrinkage and Selection via the Lasso
TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
Book ChapterDOI
Microsoft COCO: Common Objects in Context
Tsung-Yi Lin,Michael Maire,Serge Belongie,James Hays,Pietro Perona,Deva Ramanan,Piotr Dollár,C. Lawrence Zitnick +7 more
TL;DR: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.