SlideShare a Scribd company logo
1 of 59
Download to read offline
DIVING DEEP INTO SENTIMENT:
UNDERSTANDING FINE-TUNED CNNS
FOR VISUAL SENTIMENT PREDICTION
Víctor Campos Xavier Giró Amaia Salvador Brendan Jou
Outline
1. Introduction
2. Related work
3. Methodology and results
4. Conclusions
5. Future work
2
Introduction: motivation
3
4
Introduction: problem definition
▷ What?
▷ How?
▷ What? Predict the sentiment that an image provokes to a human
▷ How?
5
Introduction: problem definition
▷ What? Predict the sentiment that an image provokes to a human
▷ How?
6
Introduction: problem definition
▷ What? Predict the sentiment that an image provokes to a human
▷ How? Using Convolutional Neural Networks (CNNs)
7
CNN
Introduction: problem definition
8
CNN
Introduction: example
9
CNN
Introduction: example
Outline
1. Introduction
2. Related work
3. Methodology and results
4. Conclusions
5. Future work
10
Related work: low-level descriptors
11
Siersdorfer, S., Minack, E., Deng, F., & Hare, J. (2010, October). Analyzing
and predicting sentiment of images on the social web. In Proceedings of the
international conference on Multimedia (pp. 715-718). ACM.
Machajdik, J., & Hanbury, A. (2010, October). Affective image classification
using features inspired by psychology and art theory. In Proceedings of the
international conference on Multimedia (pp. 83-92). ACM.
12
Borth, D., Ji, R., Chen, T., Breuel, T., & Chang, S. F. (2013, October). Large-scale visual sentiment ontology and detectors using adjective
noun pairs. In Proceedings of the 21st ACM international conference on Multimedia (pp. 223-232). ACM.
Related work: SentiBank
Related work: CNNs for sentiment prediction
13
You, Q., Luo, J., Jin, H., & Yang, J. (2015). Robust image sentiment analysis using progressively trained and domain transferred deep
networks. In The Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI).
Outline
1. Introduction
2. Related work
3. Methodology and results
a. Convolutional Neural Networks
b. Datasets
c. Experimental setup and results
4. Conclusions
5. Future work
14
Convolutional Neural Networks
15
Krizhevsky, A.; Sutskever, I. & Hinton, G. E.: ImageNet Classification with Deep Convolutional Neural Networks. In: NIPS., 2012
Outline
1. Introduction
2. Related work
3. Methodology and results
a. Convolutional Neural Networks
b. Datasets
c. Experimental setup and results
4. Conclusions
5. Future work
16
Datasets
17
Flickr Twitter
Authors Borth et al. (2013) You et al. (2015)
Size ~500k 1269
Annotation method Textual tags
5 human
annotators
Datasets
18
Size
Flickr
dataset
Quality of the
annotations
Twitter
5-agree
dataset
Datasets
19
Size
Flickr
dataset
Quality of the
annotations
Twitter
5-agree
dataset
Outline
1. Introduction
2. Related work
3. Methodology and results
a. Convolutional Neural Networks
b. Datasets
c. Experimental setup and results
4. Conclusions
5. Future work
20
21
ARCHITECTURE
CaffeNet
Experimental setup: CNN
22
ARCHITECTURE
CaffeNet
SOFTWARE
[Jia’14]
Experimental setup: CNN
Experimental setup: CNN
23
Pre-trained
Model
ARCHITECTURE
CaffeNet
SOFTWARE
[Jia’14]
DATASET
[Deng’09]
Experimental setup: CNN
24
Model
ARCHITECTURE
CaffeNet
SOFTWARE
[Jia’14]
DATASET
[Deng’09]
DATASET
[You’15]
Twitter 5-agree
+
Fine-tuning
Pre-training
Experimental setup: outline
1. Fine-tuning CaffeNet
2. Layer by layer analysis
3. Layer ablation
4. Layer addition
25
Fine-tuning CaffeNet
26
Fine-tuning CaffeNet
27
Fine-tuning CaffeNet
28
Fine-tuning CaffeNet
29
Pre-trained
model
Fine-tuning CaffeNet
30
Experimental setup: outline
1. Fine-tuning CaffeNet
2. Layer by layer analysis
3. Layer ablation
4. Layer addition
31
Layer by layer analysis
32
Layer by layer analysis
33
Experimental setup: outline
1. Fine-tuning CaffeNet
2. Layer by layer analysis
3. Layer ablation
4. Layer addition
34
Layer ablation
35
Raw ablation
2-neuron on top
Layer ablation
36
Layer ablation
37
Layer ablation
38
~16M
params
(~25%)
Experimental setup: outline
1. Fine-tuning CaffeNet
2. Layer by layer analysis
3. Layer ablation
4. Layer addition
39
Layer addition
40
FC8: semantic
information
Layer addition
41
FC8: semantic
information
Outline
1. Introduction
2. Related work
3. Methodology and results
4. Conclusions
5. Future work
42
Conclusions
43
Pre-trained
model
44
CNN
Conclusions
Conclusions
45
Outline
1. Introduction
2. Related work
3. Methodology and results
4. Conclusions
5. Future work
46
Future work
47
Size
Flickr
dataset
Quality of the
annotations
Twitter
dataset
Future work
48
Size
Flickr
dataset
Quality of the
annotations
Twitter
dataset
MVSO
dataset
(†) B. Jou*, T. Chen*, N. Pappas*, M. Redi*, M. Topkara*, and S.-F. Chang. Visual Affect Around the World: A Large-scale Multilingual
Visual Sentiment Ontology. ACM Int'l Conference on Multimedia (MM), 2015.
†
49
Model
ARCHITECTURE
CaffeNet
SOFTWARE
[Jia’14]
DATASET
MVSO [Jou’15]
Future work
Acknowledgements
50
Financial supportTechnical support
Albert Gil Josep Pujal
Data augmentation (oversampling)
53
CNN
Data augmentation (oversampling)
54
CNN
Data augmentation (oversampling)
55
CNN
Data augmentation (oversampling)
56
CNN
Data augmentation (oversampling)
57
CNN
Data augmentation (oversampling)
58
CNN
Data augmentation (oversampling)
59
CNN

More Related Content

What's hot

SSII2021 [SS2] Deepfake Generation and Detection – An Overview (ディープフェイクの生成と検出)
SSII2021 [SS2] Deepfake Generation and Detection – An Overview (ディープフェイクの生成と検出)SSII2021 [SS2] Deepfake Generation and Detection – An Overview (ディープフェイクの生成と検出)
SSII2021 [SS2] Deepfake Generation and Detection – An Overview (ディープフェイクの生成と検出)
SSII
 

What's hot (20)

Deep Learning for Computer Vision: Video Analytics (UPC 2016)
Deep Learning for Computer Vision: Video Analytics (UPC 2016)Deep Learning for Computer Vision: Video Analytics (UPC 2016)
Deep Learning for Computer Vision: Video Analytics (UPC 2016)
 
Advanced Deep Architectures (D2L6 Deep Learning for Speech and Language UPC 2...
Advanced Deep Architectures (D2L6 Deep Learning for Speech and Language UPC 2...Advanced Deep Architectures (D2L6 Deep Learning for Speech and Language UPC 2...
Advanced Deep Architectures (D2L6 Deep Learning for Speech and Language UPC 2...
 
Learning with Videos (D4L4 2017 UPC Deep Learning for Computer Vision)
Learning with Videos  (D4L4 2017 UPC Deep Learning for Computer Vision)Learning with Videos  (D4L4 2017 UPC Deep Learning for Computer Vision)
Learning with Videos (D4L4 2017 UPC Deep Learning for Computer Vision)
 
Deep Learning for Computer Vision (2/4): Object Analytics @ laSalle 2016
Deep Learning for Computer Vision (2/4): Object Analytics @ laSalle 2016Deep Learning for Computer Vision (2/4): Object Analytics @ laSalle 2016
Deep Learning for Computer Vision (2/4): Object Analytics @ laSalle 2016
 
Multimodal Deep Learning (D4L4 Deep Learning for Speech and Language UPC 2017)
Multimodal Deep Learning (D4L4 Deep Learning for Speech and Language UPC 2017)Multimodal Deep Learning (D4L4 Deep Learning for Speech and Language UPC 2017)
Multimodal Deep Learning (D4L4 Deep Learning for Speech and Language UPC 2017)
 
Deep Learning - Convolutional Neural Networks - Architectural Zoo
Deep Learning - Convolutional Neural Networks - Architectural ZooDeep Learning - Convolutional Neural Networks - Architectural Zoo
Deep Learning - Convolutional Neural Networks - Architectural Zoo
 
SSII2021 [SS2] Deepfake Generation and Detection – An Overview (ディープフェイクの生成と検出)
SSII2021 [SS2] Deepfake Generation and Detection – An Overview (ディープフェイクの生成と検出)SSII2021 [SS2] Deepfake Generation and Detection – An Overview (ディープフェイクの生成と検出)
SSII2021 [SS2] Deepfake Generation and Detection – An Overview (ディープフェイクの生成と検出)
 
Intro To Convolutional Neural Networks
Intro To Convolutional Neural NetworksIntro To Convolutional Neural Networks
Intro To Convolutional Neural Networks
 
AI&BigData Lab. Артем Чернодуб "Распознавание изображений методом Lazy Deep ...
AI&BigData Lab. Артем Чернодуб  "Распознавание изображений методом Lazy Deep ...AI&BigData Lab. Артем Чернодуб  "Распознавание изображений методом Lazy Deep ...
AI&BigData Lab. Артем Чернодуб "Распознавание изображений методом Lazy Deep ...
 
Neural Architectures for Still Images - Xavier Giro- UPC Barcelona 2019
Neural Architectures for Still Images - Xavier Giro- UPC Barcelona 2019Neural Architectures for Still Images - Xavier Giro- UPC Barcelona 2019
Neural Architectures for Still Images - Xavier Giro- UPC Barcelona 2019
 
Deep Learning for Computer Vision (1/4): Image Analytics @ laSalle 2016
Deep Learning for Computer Vision (1/4): Image Analytics @ laSalle 2016Deep Learning for Computer Vision (1/4): Image Analytics @ laSalle 2016
Deep Learning for Computer Vision (1/4): Image Analytics @ laSalle 2016
 
Deep Learning Explained
Deep Learning ExplainedDeep Learning Explained
Deep Learning Explained
 
Language and Vision (D3L5 2017 UPC Deep Learning for Computer Vision)
Language and Vision (D3L5 2017 UPC Deep Learning for Computer Vision)Language and Vision (D3L5 2017 UPC Deep Learning for Computer Vision)
Language and Vision (D3L5 2017 UPC Deep Learning for Computer Vision)
 
One Perceptron to Rule Them All: Language and Vision
One Perceptron to Rule Them All: Language and VisionOne Perceptron to Rule Them All: Language and Vision
One Perceptron to Rule Them All: Language and Vision
 
Deep convnets for global recognition (Master in Computer Vision Barcelona 2016)
Deep convnets for global recognition (Master in Computer Vision Barcelona 2016)Deep convnets for global recognition (Master in Computer Vision Barcelona 2016)
Deep convnets for global recognition (Master in Computer Vision Barcelona 2016)
 
Image classification on Imagenet (D1L4 2017 UPC Deep Learning for Computer Vi...
Image classification on Imagenet (D1L4 2017 UPC Deep Learning for Computer Vi...Image classification on Imagenet (D1L4 2017 UPC Deep Learning for Computer Vi...
Image classification on Imagenet (D1L4 2017 UPC Deep Learning for Computer Vi...
 
Deep Learning - Convolutional Neural Networks
Deep Learning - Convolutional Neural NetworksDeep Learning - Convolutional Neural Networks
Deep Learning - Convolutional Neural Networks
 
Self-supervised Visual Learning 2020 - Xavier Giro-i-Nieto - UPC Barcelona
Self-supervised Visual Learning 2020 - Xavier Giro-i-Nieto - UPC BarcelonaSelf-supervised Visual Learning 2020 - Xavier Giro-i-Nieto - UPC Barcelona
Self-supervised Visual Learning 2020 - Xavier Giro-i-Nieto - UPC Barcelona
 
Il deep learning ed una nuova generazione di AI - Simone Scardapane
Il deep learning ed una nuova generazione di AI - Simone ScardapaneIl deep learning ed una nuova generazione di AI - Simone Scardapane
Il deep learning ed una nuova generazione di AI - Simone Scardapane
 
Andrew Ng, Chief Scientist at Baidu
Andrew Ng, Chief Scientist at BaiduAndrew Ng, Chief Scientist at Baidu
Andrew Ng, Chief Scientist at Baidu
 

Viewers also liked

The Unreasonable Benefits of Deep Learning
The Unreasonable Benefits of Deep LearningThe Unreasonable Benefits of Deep Learning
The Unreasonable Benefits of Deep Learning
indico data
 
12. Indexing and Hashing in DBMS
12. Indexing and Hashing in DBMS12. Indexing and Hashing in DBMS
12. Indexing and Hashing in DBMS
koolkampus
 

Viewers also liked (7)

Information retrieval dynamic indexing
Information retrieval dynamic indexingInformation retrieval dynamic indexing
Information retrieval dynamic indexing
 
Applying Machine Learning to Network Security Monitoring - BayThreat 2013
Applying Machine Learning to Network Security Monitoring - BayThreat 2013Applying Machine Learning to Network Security Monitoring - BayThreat 2013
Applying Machine Learning to Network Security Monitoring - BayThreat 2013
 
The Unreasonable Benefits of Deep Learning
The Unreasonable Benefits of Deep LearningThe Unreasonable Benefits of Deep Learning
The Unreasonable Benefits of Deep Learning
 
Elastic Search: Beyond Ordinary Fulltext Search (Webexpo 2011 Prague)
Elastic Search: Beyond Ordinary Fulltext Search (Webexpo 2011 Prague)Elastic Search: Beyond Ordinary Fulltext Search (Webexpo 2011 Prague)
Elastic Search: Beyond Ordinary Fulltext Search (Webexpo 2011 Prague)
 
Video Analysis with Recurrent Neural Networks (Master Computer Vision Barcelo...
Video Analysis with Recurrent Neural Networks (Master Computer Vision Barcelo...Video Analysis with Recurrent Neural Networks (Master Computer Vision Barcelo...
Video Analysis with Recurrent Neural Networks (Master Computer Vision Barcelo...
 
Region-oriented Convolutional Networks for Object Retrieval
Region-oriented Convolutional Networks for Object RetrievalRegion-oriented Convolutional Networks for Object Retrieval
Region-oriented Convolutional Networks for Object Retrieval
 
12. Indexing and Hashing in DBMS
12. Indexing and Hashing in DBMS12. Indexing and Hashing in DBMS
12. Indexing and Hashing in DBMS
 

Similar to Diving deep into sentiment: Understanding fine-tuned CNNs for visual sentiment prediction

Dissertation final report
Dissertation final reportDissertation final report
Dissertation final report
Smriti Tikoo
 
Reproducibility in human cognitive neuroimaging: a community-­driven data sha...
Reproducibility in human cognitive neuroimaging: a community-­driven data sha...Reproducibility in human cognitive neuroimaging: a community-­driven data sha...
Reproducibility in human cognitive neuroimaging: a community-­driven data sha...
Nolan Nichols
 
FaceDetectionforColorImageBasedonMATLAB.pdf
FaceDetectionforColorImageBasedonMATLAB.pdfFaceDetectionforColorImageBasedonMATLAB.pdf
FaceDetectionforColorImageBasedonMATLAB.pdf
Anita Pal
 

Similar to Diving deep into sentiment: Understanding fine-tuned CNNs for visual sentiment prediction (20)

323462348
323462348323462348
323462348
 
323462348
323462348323462348
323462348
 
Age and Gender Classification using Convolutional Neural Network
Age and Gender Classification using Convolutional Neural NetworkAge and Gender Classification using Convolutional Neural Network
Age and Gender Classification using Convolutional Neural Network
 
Dissertation final report
Dissertation final reportDissertation final report
Dissertation final report
 
Open-ended Visual Question-Answering
Open-ended  Visual Question-AnsweringOpen-ended  Visual Question-Answering
Open-ended Visual Question-Answering
 
Introduction to Interpretable Machine Learning
Introduction to Interpretable Machine LearningIntroduction to Interpretable Machine Learning
Introduction to Interpretable Machine Learning
 
Recent Advancements in the Field of Deepfake Detection
Recent Advancements in the Field of Deepfake DetectionRecent Advancements in the Field of Deepfake Detection
Recent Advancements in the Field of Deepfake Detection
 
Recent Advancements in the Field of Deepfake Detection
Recent Advancements in the Field of Deepfake DetectionRecent Advancements in the Field of Deepfake Detection
Recent Advancements in the Field of Deepfake Detection
 
2015 sibgrapi-contactlenses
2015 sibgrapi-contactlenses2015 sibgrapi-contactlenses
2015 sibgrapi-contactlenses
 
Ijarcce 27
Ijarcce 27Ijarcce 27
Ijarcce 27
 
Neural nets jeff_shomaker_7-6-16_
Neural nets jeff_shomaker_7-6-16_Neural nets jeff_shomaker_7-6-16_
Neural nets jeff_shomaker_7-6-16_
 
Machine Learning.pptx
Machine Learning.pptxMachine Learning.pptx
Machine Learning.pptx
 
Age and gender detection using deep learning
Age and gender detection using deep learningAge and gender detection using deep learning
Age and gender detection using deep learning
 
Reproducibility in human cognitive neuroimaging: a community-­driven data sha...
Reproducibility in human cognitive neuroimaging: a community-­driven data sha...Reproducibility in human cognitive neuroimaging: a community-­driven data sha...
Reproducibility in human cognitive neuroimaging: a community-­driven data sha...
 
Parkinson’s Disease Detection Using Transfer Learning
Parkinson’s Disease Detection Using Transfer LearningParkinson’s Disease Detection Using Transfer Learning
Parkinson’s Disease Detection Using Transfer Learning
 
FaceDetectionforColorImageBasedonMATLAB.pdf
FaceDetectionforColorImageBasedonMATLAB.pdfFaceDetectionforColorImageBasedonMATLAB.pdf
FaceDetectionforColorImageBasedonMATLAB.pdf
 
Ijsrdv8 i10424
Ijsrdv8 i10424Ijsrdv8 i10424
Ijsrdv8 i10424
 
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep NetworksModel-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
 
Introduction to Soft Computing
Introduction to Soft Computing Introduction to Soft Computing
Introduction to Soft Computing
 
Data visualisations: drawing actionable insights from science and technology ...
Data visualisations: drawing actionable insights from science and technology ...Data visualisations: drawing actionable insights from science and technology ...
Data visualisations: drawing actionable insights from science and technology ...
 

More from Universitat Politècnica de Catalunya

Generation of Synthetic Referring Expressions for Object Segmentation in Videos
Generation of Synthetic Referring Expressions for Object Segmentation in VideosGeneration of Synthetic Referring Expressions for Object Segmentation in Videos
Generation of Synthetic Referring Expressions for Object Segmentation in Videos
Universitat Politècnica de Catalunya
 

More from Universitat Politècnica de Catalunya (20)

Deep Generative Learning for All - The Gen AI Hype (Spring 2024)
Deep Generative Learning for All - The Gen AI Hype (Spring 2024)Deep Generative Learning for All - The Gen AI Hype (Spring 2024)
Deep Generative Learning for All - The Gen AI Hype (Spring 2024)
 
Deep Generative Learning for All
Deep Generative Learning for AllDeep Generative Learning for All
Deep Generative Learning for All
 
The Transformer in Vision | Xavier Giro | Master in Computer Vision Barcelona...
The Transformer in Vision | Xavier Giro | Master in Computer Vision Barcelona...The Transformer in Vision | Xavier Giro | Master in Computer Vision Barcelona...
The Transformer in Vision | Xavier Giro | Master in Computer Vision Barcelona...
 
Towards Sign Language Translation & Production | Xavier Giro-i-Nieto
Towards Sign Language Translation & Production | Xavier Giro-i-NietoTowards Sign Language Translation & Production | Xavier Giro-i-Nieto
Towards Sign Language Translation & Production | Xavier Giro-i-Nieto
 
The Transformer - Xavier Giró - UPC Barcelona 2021
The Transformer - Xavier Giró - UPC Barcelona 2021The Transformer - Xavier Giró - UPC Barcelona 2021
The Transformer - Xavier Giró - UPC Barcelona 2021
 
Learning Representations for Sign Language Videos - Xavier Giro - NIST TRECVI...
Learning Representations for Sign Language Videos - Xavier Giro - NIST TRECVI...Learning Representations for Sign Language Videos - Xavier Giro - NIST TRECVI...
Learning Representations for Sign Language Videos - Xavier Giro - NIST TRECVI...
 
Open challenges in sign language translation and production
Open challenges in sign language translation and productionOpen challenges in sign language translation and production
Open challenges in sign language translation and production
 
Generation of Synthetic Referring Expressions for Object Segmentation in Videos
Generation of Synthetic Referring Expressions for Object Segmentation in VideosGeneration of Synthetic Referring Expressions for Object Segmentation in Videos
Generation of Synthetic Referring Expressions for Object Segmentation in Videos
 
Discovery and Learning of Navigation Goals from Pixels in Minecraft
Discovery and Learning of Navigation Goals from Pixels in MinecraftDiscovery and Learning of Navigation Goals from Pixels in Minecraft
Discovery and Learning of Navigation Goals from Pixels in Minecraft
 
Learn2Sign : Sign language recognition and translation using human keypoint e...
Learn2Sign : Sign language recognition and translation using human keypoint e...Learn2Sign : Sign language recognition and translation using human keypoint e...
Learn2Sign : Sign language recognition and translation using human keypoint e...
 
Intepretability / Explainable AI for Deep Neural Networks
Intepretability / Explainable AI for Deep Neural NetworksIntepretability / Explainable AI for Deep Neural Networks
Intepretability / Explainable AI for Deep Neural Networks
 
Convolutional Neural Networks - Xavier Giro - UPC TelecomBCN Barcelona 2020
Convolutional Neural Networks - Xavier Giro - UPC TelecomBCN Barcelona 2020Convolutional Neural Networks - Xavier Giro - UPC TelecomBCN Barcelona 2020
Convolutional Neural Networks - Xavier Giro - UPC TelecomBCN Barcelona 2020
 
Self-Supervised Audio-Visual Learning - Xavier Giro - UPC TelecomBCN Barcelon...
Self-Supervised Audio-Visual Learning - Xavier Giro - UPC TelecomBCN Barcelon...Self-Supervised Audio-Visual Learning - Xavier Giro - UPC TelecomBCN Barcelon...
Self-Supervised Audio-Visual Learning - Xavier Giro - UPC TelecomBCN Barcelon...
 
Attention for Deep Learning - Xavier Giro - UPC TelecomBCN Barcelona 2020
Attention for Deep Learning - Xavier Giro - UPC TelecomBCN Barcelona 2020Attention for Deep Learning - Xavier Giro - UPC TelecomBCN Barcelona 2020
Attention for Deep Learning - Xavier Giro - UPC TelecomBCN Barcelona 2020
 
Generative Adversarial Networks GAN - Xavier Giro - UPC TelecomBCN Barcelona ...
Generative Adversarial Networks GAN - Xavier Giro - UPC TelecomBCN Barcelona ...Generative Adversarial Networks GAN - Xavier Giro - UPC TelecomBCN Barcelona ...
Generative Adversarial Networks GAN - Xavier Giro - UPC TelecomBCN Barcelona ...
 
Q-Learning with a Neural Network - Xavier Giró - UPC Barcelona 2020
Q-Learning with a Neural Network - Xavier Giró - UPC Barcelona 2020Q-Learning with a Neural Network - Xavier Giró - UPC Barcelona 2020
Q-Learning with a Neural Network - Xavier Giró - UPC Barcelona 2020
 
Language and Vision with Deep Learning - Xavier Giró - ACM ICMR 2020 (Tutorial)
Language and Vision with Deep Learning - Xavier Giró - ACM ICMR 2020 (Tutorial)Language and Vision with Deep Learning - Xavier Giró - ACM ICMR 2020 (Tutorial)
Language and Vision with Deep Learning - Xavier Giró - ACM ICMR 2020 (Tutorial)
 
Image Segmentation with Deep Learning - Xavier Giro & Carles Ventura - ISSonD...
Image Segmentation with Deep Learning - Xavier Giro & Carles Ventura - ISSonD...Image Segmentation with Deep Learning - Xavier Giro & Carles Ventura - ISSonD...
Image Segmentation with Deep Learning - Xavier Giro & Carles Ventura - ISSonD...
 
Curriculum Learning for Recurrent Video Object Segmentation
Curriculum Learning for Recurrent Video Object SegmentationCurriculum Learning for Recurrent Video Object Segmentation
Curriculum Learning for Recurrent Video Object Segmentation
 
Deep Self-supervised Learning for All - Xavier Giro - X-Europe 2020
Deep Self-supervised Learning for All - Xavier Giro - X-Europe 2020Deep Self-supervised Learning for All - Xavier Giro - X-Europe 2020
Deep Self-supervised Learning for All - Xavier Giro - X-Europe 2020
 

Recently uploaded

Tales from a Passkey Provider Progress from Awareness to Implementation.pptx
Tales from a Passkey Provider  Progress from Awareness to Implementation.pptxTales from a Passkey Provider  Progress from Awareness to Implementation.pptx
Tales from a Passkey Provider Progress from Awareness to Implementation.pptx
FIDO Alliance
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Safe Software
 

Recently uploaded (20)

Design Guidelines for Passkeys 2024.pptx
Design Guidelines for Passkeys 2024.pptxDesign Guidelines for Passkeys 2024.pptx
Design Guidelines for Passkeys 2024.pptx
 
Simplifying Mobile A11y Presentation.pptx
Simplifying Mobile A11y Presentation.pptxSimplifying Mobile A11y Presentation.pptx
Simplifying Mobile A11y Presentation.pptx
 
Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)
 
Less Is More: Utilizing Ballerina to Architect a Cloud Data Platform
Less Is More: Utilizing Ballerina to Architect a Cloud Data PlatformLess Is More: Utilizing Ballerina to Architect a Cloud Data Platform
Less Is More: Utilizing Ballerina to Architect a Cloud Data Platform
 
TEST BANK For Principles of Anatomy and Physiology, 16th Edition by Gerard J....
TEST BANK For Principles of Anatomy and Physiology, 16th Edition by Gerard J....TEST BANK For Principles of Anatomy and Physiology, 16th Edition by Gerard J....
TEST BANK For Principles of Anatomy and Physiology, 16th Edition by Gerard J....
 
Six Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal OntologySix Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal Ontology
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
 
ADP Passwordless Journey Case Study.pptx
ADP Passwordless Journey Case Study.pptxADP Passwordless Journey Case Study.pptx
ADP Passwordless Journey Case Study.pptx
 
Vector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxVector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptx
 
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 AmsterdamDEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
 
Stronger Together: Developing an Organizational Strategy for Accessible Desig...
Stronger Together: Developing an Organizational Strategy for Accessible Desig...Stronger Together: Developing an Organizational Strategy for Accessible Desig...
Stronger Together: Developing an Organizational Strategy for Accessible Desig...
 
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024
 
Intro to Passkeys and the State of Passwordless.pptx
Intro to Passkeys and the State of Passwordless.pptxIntro to Passkeys and the State of Passwordless.pptx
Intro to Passkeys and the State of Passwordless.pptx
 
API Governance and Monetization - The evolution of API governance
API Governance and Monetization -  The evolution of API governanceAPI Governance and Monetization -  The evolution of API governance
API Governance and Monetization - The evolution of API governance
 
Tales from a Passkey Provider Progress from Awareness to Implementation.pptx
Tales from a Passkey Provider  Progress from Awareness to Implementation.pptxTales from a Passkey Provider  Progress from Awareness to Implementation.pptx
Tales from a Passkey Provider Progress from Awareness to Implementation.pptx
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
 
WSO2 Micro Integrator for Enterprise Integration in a Decentralized, Microser...
WSO2 Micro Integrator for Enterprise Integration in a Decentralized, Microser...WSO2 Micro Integrator for Enterprise Integration in a Decentralized, Microser...
WSO2 Micro Integrator for Enterprise Integration in a Decentralized, Microser...
 
WSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering DevelopersWSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering Developers
 
UiPath manufacturing technology benefits and AI overview
UiPath manufacturing technology benefits and AI overviewUiPath manufacturing technology benefits and AI overview
UiPath manufacturing technology benefits and AI overview
 

Diving deep into sentiment: Understanding fine-tuned CNNs for visual sentiment prediction