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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Proceedings ArticleDOI
Learning a Deep Embedding Model for Zero-Shot Learning
Li Zhang,Tao Xiang,Shaogang Gong +2 more
TL;DR: This paper proposes to use the visual space as the embedding space instead of embedding into a semantic space or an intermediate space, and argues that in this space, the subsequent nearest neighbour search would suffer much less from the hubness problem and thus become more effective.
Book ChapterDOI
LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks
TL;DR: LQ-Nets as mentioned in this paper proposes to jointly train a quantized, bit-operation-compatible DNN and its associated quantizers, as opposed to using fixed, handcrafted quantization schemes such as uniform or logarithmic quantization.
Proceedings ArticleDOI
Rethinking the Faster R-CNN Architecture for Temporal Action Localization
Yu-Wei Chao,Sudheendra Vijayanarasimhan,Bryan Seybold,David A. Ross,Jia Deng,Rahul Sukthankar +5 more
TL;DR: TAL-Net as mentioned in this paper improves receptive field alignment using a multi-scale architecture that can accommodate extreme variation in action durations and better exploit the temporal context of actions for both proposal generation and action classification by appropriately extending receptive fields.
Proceedings ArticleDOI
Image Generation from Scene Graphs
TL;DR: This work proposes a method for generating images from scene graphs, enabling explicitly reasoning about objects and their relationships, and validates this approach on Visual Genome and COCO-Stuff.
Proceedings ArticleDOI
Semantic Autoencoder for Zero-Shot Learning
TL;DR: In this paper, an encoder aims to project a visual feature vector into the semantic space as in the existing ZSL models, but the decoder exerts an additional constraint, that the projection/code must be able to reconstruct the original visual feature.
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
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
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.