Open AccessProceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TLDR
In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.Abstract:
In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.read more
Citations
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GelSight: High-Resolution Robot Tactile Sensors for Estimating Geometry and Force.
TL;DR: This paper reviews the development of GelSight, with the emphasis in the sensing principle and sensor design, and introduces the design of the sensor’s optical system, the algorithm for shape, force and slip measurement, and the hardware designs and fabrication of different sensor versions.
Proceedings ArticleDOI
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation
TL;DR: In this article, the authors propose a competitive collaboration framework that facilitates the coordinated training of multiple specialized neural networks to solve complex low-level vision problems, such as single view depth prediction, camera motion estimation, optical flow, and segmentation of a video into the static scene and moving regions.
Proceedings ArticleDOI
Boosting Image Captioning with Attributes
TL;DR: Li et al. as discussed by the authors proposed a Long Short-Term Memory with Attributes (LSTM-A) architecture that integrates attributes into the successful Convolutional Neural Networks (CNNs) plus RNNs (RNNs) image captioning framework, by training them in an end-to-end manner.
Proceedings ArticleDOI
Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes
TL;DR: In this paper, a ResNet-like architecture is proposed to combine multi-scale context with pixel-level accuracy by using two processing streams within the network: one stream carries information at the full image resolution and the other stream undergoes a sequence of pooling operations to obtain robust features for recognition.
Journal ArticleDOI
Towards artificial general intelligence with hybrid Tianjic chip architecture.
Jing Pei,Lei Deng,Sen Song,Sen Song,Mingguo Zhao,Youhui Zhang,Shuang Wu,Guanrui Wang,Zhe Zou,Zhenzhi Wu,Wei He,Feng Chen,Ning Deng,Si Wu,Yu Wang,Yujie Wu,Z. Yang,Cheng Ma,Guoqi Li,Wentao Han,Huanglong Li,Huaqiang Wu,Rong Zhao,Yuan Xie,Luping Shi +24 more
TL;DR: The Tianjic chip is presented, which integrates neuroscience-oriented and computer-science-oriented approaches to artificial general intelligence to provide a hybrid, synergistic platform and is expected to stimulate AGI development by paving the way to more generalized hardware platforms.
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.
Proceedings ArticleDOI
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: 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).