Open AccessProceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
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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
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Switching Convolutional Neural Network for Crowd Counting
TL;DR: In this paper, the authors propose a switching convolutional neural network that leverages variation of crowd density within an image to improve the accuracy and localization of the predicted crowd count, and provide interpretable representations of the multichotomy of space of crowd scene patches inferred from the switch.
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Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear Units
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Book ChapterDOI
The Visual Object Tracking VOT2016 Challenge Results
Matej Kristan,Ales Leonardis,Jiří Matas,Michael Felsberg,Roman Pflugfelder,Luka Cehovin,Tomas Vojir,Gustav Häger,Alan Lukežič,Gustavo Fernandez,Abhinav Gupta,Alfredo Petrosino,Alireza Memarmoghadam,Alvaro Garcia-Martin,Andres Solis Montero,Andrea Vedaldi,Andreas Robinson,Andy J. Ma,Anton Varfolomieiev,A. Aydin Alatan,Aykut Erdem,Bernard Ghanem,Bin Liu,Bohyung Han,Brais Martinez,Chang-Ming Chang,Changsheng Xu,Chong Sun,Daijin Kim,Dapeng Chen,Dawei Du,Deepak Mishra,Dit-Yan Yeung,Erhan Gundogdu,Erkut Erdem,Fahad Shahbaz Khan,Fatih Porikli,Fatih Porikli,Fei Zhao,Filiz Bunyak,Francesco Battistone,Gao Zhu,Giorgio Roffo,Gorthi R. K. Sai Subrahmanyam,Guilherme Sousa Bastos,Guna Seetharaman,Henry Medeiros,Hongdong Li,Honggang Qi,Horst Bischof,Horst Possegger,Huchuan Lu,Hyemin Lee,Hyeonseob Nam,Hyung Jin Chang,Isabela Drummond,Jack Valmadre,Jae-chan Jeong,Jaeil Cho,Jae-Yeong Lee,Jianke Zhu,Jiayi Feng,Jin Gao,Jin-Young Choi,Jingjing Xiao,Ji-Wan Kim,Jiyeoup Jeong,João F. Henriques,Jochen Lang,Jongwon Choi,José M. Martínez,Junliang Xing,Junyu Gao,Kannappan Palaniappan,Karel Lebeda,Ke Gao,Krystian Mikolajczyk,Lei Qin,Lijun Wang,Longyin Wen,Luca Bertinetto,Madan Kumar Rapuru,Mahdieh Poostchi,Mario Edoardo Maresca,Martin Danelljan,Matthias Mueller,Mengdan Zhang,Michael Arens,Michel Valstar,Ming Tang,Mooyeol Baek,Muhammad Haris Khan,Naiyan Wang,Nana Fan,Noor M. Al-Shakarji,Ondrej Miksik,Osman Akin,Payman Moallem,Pedro Senna,Philip H. S. Torr,Pong C. Yuen,Qingming Huang,Qingming Huang,Rafael Martin-Nieto,Rengarajan Pelapur,Richard Bowden,Robert Laganiere,Rustam Stolkin,Ryan Walsh,Sebastian B. Krah,Shengkun Li,Shengping Zhang,Shizeng Yao,Simon Hadfield,Simone Melzi,Siwei Lyu,Siyi Li,Stefan Becker,Stuart Golodetz,Sumithra Kakanuru,Sunglok Choi,Tao Hu,Thomas Mauthner,Tianzhu Zhang,Tony P. Pridmore,Vincenzo Santopietro,Weiming Hu,Wenbo Li,Wolfgang Hübner,Xiangyuan Lan,Xiaomeng Wang,Xin Li,Yang Li,Yiannis Demiris,Yifan Wang,Yuankai Qi,Zejian Yuan,Zexiong Cai,Zhan Xu,Zhenyu He,Zhizhen Chi +140 more
TL;DR: The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment.
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).