PDF-Largescale Video Classication with Convolutional Neura

stanfordedu gtodericigooglecom sankethgooglecom Thomas Leung Rahul Sukthankar Li FeiFei leungtgooglecom sukthankargooglecom feifeilicsstanfordedu Google Research Computer Science Department Stanford University httpcsstanfordedupeoplekarpathydeepvideo. Abstract In this paper we study how to perform object classi64257cation in a principled way that exploits the rich structure of real world labels We develop a new model that allows encoding of 64258exible relations between labels We introduce Hierar RECOGNITION. does size matter?. Karen . Simonyan. Andrew . Zisserman. Contents. Why I Care. Introduction. Convolutional Configuration . Classification. Experiments. Conclusion. Big Picture. Why I . care. Zhenjiang Li, . Yaxiong. . Xie. , Mo Li, . Nanyang Technological University. Kyle . Jamieson. University College . London. Up to . 160. MHz. Up to . 40. MHz. Up to . 22. MHz. 802.11 a/b/g. (. 1999. patch sampling with deep convolutional neural networks for white matter hyperintensity segmentation. Mohsen Ghafoorian. a,b. , Nico Karssemeijer. a. , Inge van Uden. c. , Frank-Erik de Leeuw. c. , Tom Heskes. Sergey Zagoruyko & Nikos Komodakis. Introduction. Comparing Patches across images is one of the most fundamental tasks in computer vision. Applications include structure from motion, wide baseline matching and building panorama. Ross Girshick. Microsoft Research. Guest lecture for UW CSE 455. Nov. 24, 2014. Outline. Object detection. the task, evaluation, datasets. Convolutional Neural Networks (CNNs). overview and history. Region-based Convolutional Networks (R-CNNs). Abhinav . Podili. , Chi Zhang, Viktor . Prasanna. Ming Hsieh Department of Electrical Engineering. University of Southern California. {. podili. , zhan527, . prasanna. }@usc.edu. fpga.usc.edu. ASAP, July 2017. Last time. Linear classifiers on pixels bad, need non-linear classifiers. Multi-layer . perceptrons. . overparametrized. Reduce parameters by local connections and shift invariance => Convolution. boris. . ginzburg@intel.com. Agenda. Introduction to gradient-based learning for Convolutional NN. Backpropagation. for basic layers. Softmax. Fully Connected layer. Pooling. ReLU. Convolutional layer. person 1. person 2. horse 1. horse 2. R-CNN: Regions with CNN features. Input. image. Extract region. proposals (~2k / image). Compute CNN. features. Classify regions. (linear SVM). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Article and Work by. : Justin . Salamon. and Juan Pablo Bello. Presented by . : . Dhara. Rana. Overall Goal of Paper. Create a way to classify environmental sound given an audio clip. Other methods of sound classification: (1) dictionary learning and (2) wavelet filter banks . Convolutional Codes COS 463 : Wireless Networks Lecture 9 Kyle Jamieson [Parts adapted from H. Balakrishnan ] So far, we’ve seen block codes Convolutional Codes: Simple design, especially at the transmitter Prabhas. . Chongstitvatana. Faculty of Engineering. Chulalongkorn. university. More Information. Search “Prabhas Chongstitvatana”. Go to me homepage. Perceptron. Rosenblatt, 1950. Multi-layer perceptron. n,k. ) code by adding the r parity digits. An alternative scheme that groups the data stream into much smaller blocks k digits and encode them into n digits with order of k say 1, 2 or 3 digits at most is the convolutional codes. Such code structure can be realized using convolutional structure for the data digits..