Fully Convolutional Network (FCN) Prof. Seungchul Lee Industrial AI - PowerPoint PPT Presentation

Oct 20, 2022 •354 likes •522 views

Fully Convolutional Network (FCN) Prof. Seungchul Lee Industrial AI Lab. Deep Learning for Computer Vision: Review Source: 6.S191 Intro. to Deep Learning at MIT 2 Segmentation Segmentation task is different from classification task because

Fully Convolutional Network (FCN) Prof. Seungchul Lee Industrial AI Lab.
Deep Learning for Computer Vision: Review Source: 6.S191 Intro. to Deep Learning at MIT 2
Segmentation • Segmentation task is different from classification task because it requires predicting a class for each pixel of the input image, instead of only 1 class for the whole input. • Segment images into regions with different semantic categories. These semantic regions label and predict objects at the pixel level Image from http://d2l.ai/ 3
Segmentation • Segmentation task is different from classification task because it requires predicting a class for each pixel of the input image, instead of only 1 class for the whole input. • Segment images into regions with different semantic categories. These semantic regions label and predict objects at the pixel level • Classification needs to understand what is in the input (namely, the context). • However, in order to predict what is in the input for each pixel, segmentation needs to recover not only what is in the input, but also where. Image from http://d2l.ai/ 4
Semantic Segmentation: FCNs • FCN uses a convolutional neural network to transform image pixels to pixel categories. • Network designed with all convolutional layers, with down-sampling and up-sampling operations • Given a position on the spatial dimension, the output of the channel dimension will be a category prediction of the pixel corresponding to the location. Image from http://d2l.ai/ 5
From CAE to FCN 6
From CAE to FCN 7
Skip Connection • A skip connection is a connection that bypasses at least one layer. • Here, it is often used to transfer local information by summing feature maps from the downsampling path with feature maps from the upsampling path. – Merging features from various resolution levels helps combining context information with spatial information. 8
Fully Convolutional Networks (FCNs) • To obtain a segmentation map (output), segmentation networks usually have 2 parts – Downsampling path: capture semantic/contextual information – Upsampling path: recover spatial information • The downsampling path is used to extract and interpret the context (what), while the upsampling path is used to enable precise localization (where). • Furthermore, to fully recover the fine-grained spatial information lost in the pooling or downsampling layers, we often use skip connections. • Network can work regardless of the original image size, without requiring any fixed number of units at any stage. 9
Segmented (Labeled) Images input output output 10
FCN Architecture maxp3 maxp4 fcn4 fcn3 fcn2 fcn1 Fixed Trained 11
FCN Architecture maxp3 maxp4 fcn4 fcn3 fcn2 fcn1 Fixed Trained 12
FCN Architecture maxp3 maxp4 fcn4 fcn3 fcn2 fcn1 Fixed Trained 13
FCN Architecture maxp3 maxp4 fcn4 fcn3 fcn2 fcn1 Fixed Trained 14
Segmentation Result maxp3 maxp4 15

Recommend

Neural Network Basics Part II Content Image-to-image Why fully convolutional?

Neural Network Basics Part II Content Image-to-image Why fully convolutional? Fully Convolutional Networks (FCN) Up-sampling Network architecture Recurrent Neural Networks Sequence data and representation

625 views • 45 slides

S 3 D: S ingle S hot multi- S pan D etector via Fully 3D Convolutional Network Da Zhang 1 ,

S 3 D: S ingle S hot multi- S pan D etector via Fully 3D Convolutional Network Da Zhang 1 , Xiyang Dai 2 , Xin Wang 1 , and Yuan-Fang Wang 1 dazhang@cs.ucsb.edu 1 UC Santa Barbara & 2 University of Maryland Task: Temporal Activity Detection

391 views • 21 slides

ON TEGRA X1 ALAN WANG, NVIDIA Convolutional Neural Network optimization target Result

DIRECT CONVOLUTION FOR DEEP NEURAL NETWORK CLASSIFICATION ON TEGRA X1 ALAN WANG, NVIDIA Convolutional Neural Network optimization target Result Convolutional Fully Connected Input layer layer Convolutional Layer An example: A E

621 views • 18 slides

The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation Simon

The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation Simon Jegou , Michal Drozdzal, David Vazquez, Adriana Romero Yoshua Bengio 1 Deep Neural Network use a cascade of multiple layers of units for

640 views • 29 slides

Convolutional Autoencoder (CAE) Prof. Seungchul Lee Industrial AI Lab. Convolutional Autoencoder

Convolutional Autoencoder (CAE) Prof. Seungchul Lee Industrial AI Lab. Convolutional Autoencoder Motivation: image to autoencoder ? Convolutional autoencoder extends the basic structure of the simple autoencoder by changing the fully

349 views • 15 slides

Convolutional Kuan-Ting Lai 2020/3/31 Neural Network Convolutional Neural Networks (CNN)

Convolutional Kuan-Ting Lai 2020/3/31 Neural Network Convolutional Neural Networks (CNN) A.k.a. CNN or ConvNet Adit Deshpande, A Beginner's Guide To Understanding Convolutional Neural Networks. Digital Images Input array: an images

1.42k views • 72 slides

CONVOLUTIONAL AND RECURRENT NEURAL NETWORKS Neural networks Fully connected networks

CONVOLUTIONAL AND RECURRENT NEURAL NETWORKS Neural networks Fully connected networks Neuron Non-linearity Softmax layer DNN training Loss function and regularization SGD and backprop Learning rate Overfitting

1.8k views • 86 slides

Fully Convolutional Networks for Handwriting Recognition Felipe Petroski Such, Dheeraj Peri,

Fully Convolutional Networks for Handwriting Recognition Felipe Petroski Such*, Dheeraj Peri*, Frank Brockler, Paul Hutkowski, Raymond Ptucha* *Rochester Institute of Technology, Kodak Alaris, 1 Such et al. ICFHR18 Background

579 views • 20 slides

Cascaded 3D Fully Convolutional Networks for Medical Image Segmentation Holger Roth Assistant

Cascaded 3D Fully Convolutional Networks for Medical Image Segmentation Holger Roth Assistant Professor (Research) Nagoya University, Japan Contributors: Hirohisa Oda a , Xiangrong Zhou b , Natsuki Shimizu a , Ying Yang a , Chen Shen a ,

389 views • 25 slides

A Fuller Understanding of Fully Convolutional Networks Evan Shelhamer* Jonathan Long* Trevor

A Fuller Understanding of Fully Convolutional Networks Evan Shelhamer* Jonathan Long* Trevor Darrell UC Berkeley in CVPR'15, PAMI'16 1 pixels in, pixels out colorization Zhang et al.2016 monocular depth + normals Eigen & Fergus 2015

414 views • 38 slides

A Normalized Fully Convolutional Approach to Head and Neck Cancer Outcome Prediction William

A Normalized Fully Convolutional Approach to Head and Neck Cancer Outcome Prediction William Le, Francisco Perdigon Romero and Samuel Kadoury from MediCAL lab, CRCHUM Canada Research Chair in Medical Imaging and Assisted Interventions

177 views • 6 slides

Convolutional Neural Networks in Speech Lecture 20 CS 753 Instructor: Preethi Jyothi

Convolutional Neural Networks in Speech Lecture 20 CS 753 Instructor: Preethi Jyothi Convolutional Neural Networks (CNNs) Fully connected (dense) layers have no awareness of spatial information Key concept behind convolutional layers is

983 views • 41 slides

Fully-Convolutional Siamese Networks for Object Tracking Luca Bertinetto, Jack Valmadre, Joo

Fully-Convolutional Siamese Networks for Object Tracking Luca Bertinetto*, Jack Valmadre*, Joo Henriques, Andrea Vedaldi and Philip Torr www.robots.ox.ac.uk/~luca luca.bertinetto@eng.ox.ac.uk Tracking of single, arbitrary objects Problem .

514 views • 19 slides

Convolutional Neural Networks M. Soleymani Sharif University of Technology Fall 2017 Slides

Convolutional Neural Networks M. Soleymani Sharif University of Technology Fall 2017 Slides have been adopted from Fei Fei Li and colleagues lectures and notes, cs231n, Stanford 2017. Fully connected layer Fully connected layers Neurons

680 views • 55 slides

Convolutional Neural Nets 4-25-16 Reading Quiz Convolutional neural networks are most commonly

Convolutional Neural Nets 4-25-16 Reading Quiz Convolutional neural networks are most commonly used for what sort of task? a) Recognizing images b) Transcribing speech c) Translating documents d) Playing games Neural network review

431 views • 13 slides

Outline Convolutional Neural Network Architectures for Matching Natural Language Sentences.

Outline Hu, NIPS14 Irsoy, NIPS14 Outline Convolutional Neural Network Architectures for Matching Natural Language Sentences. NIPS14 Convolutional Sentence Model Convolutional Matching Models Experiments Deep Recursive Neural

779 views • 35 slides

SEMANTIC IMAGE SEGMENTATION WITH DEEP CONVOLUTIONAL NETS AND FULLY CONNECTED CRFS Paper by Chen,

SEMANTIC IMAGE SEGMENTATION WITH DEEP CONVOLUTIONAL NETS AND FULLY CONNECTED CRFS Paper by Chen, Papandreou, Kokkinos, Murphy, Yuille Slides by Josh Kelle (with graphics from the paper) Semantic Segmentation Goal: Partition the image into

520 views • 22 slides

Count-ception: Counting by Fully Convolutional Redundant Counting Joseph Paul Cohen - MILA,

Count-ception: Counting by Fully Convolutional Redundant Counting Joseph Paul Cohen - MILA, University of Montreal Genevive Boucher - IRIC, University of Montreal Craig A. Glastonbury - BDI, University of Oxford Henry Z. Lo -

859 views • 51 slides

Convolutional Neural Networks Basics Praveen Krishnan Overview Paradigm Shift Simple

Convolutional Neural Networks Basics Praveen Krishnan Overview Paradigm Shift Simple Network Convolutional Network Layers Case Study1: Alex Net Training Generalization Visualizations Transfer Learning Case

1.02k views • 53 slides

Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting LSTM

Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting LSTM : VALSE 2016/03/23 Content Quick Review of Recurrent Neural Network

738 views • 27 slides

U-Finger Multi-Scale Dilated Convolutional Network for Fingerprint Image Denoising and Inpainting

U-Finger Multi-Scale Dilated Convolutional Network for Fingerprint Image Denoising and Inpainting Ramakrishna Prabhu, Xiaojing Yu, Zhangyang Wang, Ding Liu, Anxiao (Andrew) Jiang Why Deep Neural Network ? Fingerprint restoration and

321 views • 20 slides

CS4501: Introduction to Computer Vision Deeper Convolutional Neural Network Architectures Last

CS4501: Introduction to Computer Vision Deeper Convolutional Neural Network Architectures Last Class Neural Networks multilayer perceptron model (MLP) Backpropagation Convolutional Neural Networks Todays Class More on

772 views • 41 slides

Adaptive Distributed Convolutional Neural Network Inference at the Network Edge with ADCNN 17

Adaptive Distributed Convolutional Neural Network Inference at the Network Edge with ADCNN 17 August 2020 ICPP 2020 Sai Qian Zhang , Jieyu Lin, Qi Zhang Executing DNN Inference Tasks for End Users Option 2: cloud only Option 1: edge only

579 views • 17 slides

Dependency-based Convolutional Neural Networks for Sentence Embedding What is Hawaii

Dependency-based Convolutional Neural Networks for Sentence Embedding What is Hawaii s state flower ? ROOT Mingbo Ma Liang Huang Bing Xiang Bowen Zhou CUNY IBM T. J. Watson ACL 2015 Beijing Convolutional Neural Network

742 views • 50 slides