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Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. Architectures such as Convolutional Neural Networks (CNNs) and more recently the Transformer based on Attention mechanisms are solving many problem in the Computer Vision field. This course will cover the principles of deep learning applied to different Computer Vision applications as well as covering the basics of Multimodal Learning.
The course webpage can be found: [2019], [2018], [2017], [2016]
DLCV Lectures (2018)
DLCV Lectures (2018)
dlcv_2018_d1l2_theneuralnetworkzoo.pdfLecture 1.1: Neural Network Zoo
Lecture 1.1: Neural Network Zoo
Learning Paradigms
Instructor: Xavier Giró-i-Nieto
dlcv_2018_d1l3_imageclassification.pdfLecture 1.2: Image Classification
Lecture 1.2: Image Classification
Computer Vision
Instructor: Kevin McGuinness
dlcv_2018_d1l4_imageretrieval.pdfLecture 1.3: Image Retrieval
Lecture 1.3: Image Retrieval
Computer Vision
Instructor: Eva Mohedano
dlcv_2018_d1l5_visuallocalization.pdfLecture 1.4: Visual Localization
Lecture 1.4: Visual Localization
Computer Vision
Instructor: Laura Leal-Taixé
dlcv_2018_d1l6_videosegmentation.pdfLecture 1.5: Video Object Segmentation
Lecture 1.5: Video Object Segmentation
Computer Vision
Instructor: Laura Leal Taixé
dlcv_2018_d2l1_objectdetection.pdfLecture 2.1: Object Detection
Lecture 2.1: Object Detection
Computer Vision
Instructor: Miriam Bellver
dlcv_2018_d2l2_facerecognition.pdfLecture 2.2: Face Detection & Recognition
Lecture 2.2: Face Detection & Recognition
Computer Vision
Instructor: Elisa Sayrol
dlcv_2018_d2l3_semanticsegmentation.pdfLecture 2.3: Semantic Segmentation
Lecture 2.3: Semantic Segmentation
Computer Vision
Instructor: Miriam Bellver
dlcv_2018_d2l4_instancesegmentation.pdfLecture 2.4: Instance Segmentation
Lecture 2.4: Instance Segmentation
Computer Vision
Instructor: Miriam Bellver
dlcv_2018_d2l5_medicalimaging.pdfLecture 2.5: Medical Imaging
Lecture 2.5: Medical Imaging
Computer Vision
Instructor: Elisa Sayrol
dlcv_2018_d3l1_2_videoanalysis.pdfLecture 3.1 & 3.2: Video Analysis
Lecture 3.1 & 3.2: Video Analysis
Computer Vision
Instructor: Victor Campos
dlcv_2018_d3l3_objecttracking.pdfLecture 3.3: Object Tracking
Lecture 3.3: Object Tracking
Computer Vision
Instructor: Laura Leal Taixé
dlcv_2018_d3l4_interpretability.pdfLecture 3.4: Interpretability
Lecture 3.4: Interpretability
Computer Vision
Instructor: Eva Mohedano
dlcv_2018_d3l5_saliencyprediction.pdfLecture 3.5: Saliency Prediction
Lecture 3.5: Saliency Prediction
Computer Vision
Instructor: Kevin McGuinness
dlcv_2018_d3l6_setlearning.pdfLecture 3.6: Set Learning
Lecture 3.6: Set Learning
Computer Vision
Instructor: Laura Leal Taixé
dlcv_2018_d4l1_3danalysis.pdfLecture 4.1: 3D Analysis
Lecture 4.1: 3D Analysis
Computer Vision
Instructor: Javier Ruiz
dlcv_2018_d4l2_3dreconstruction.pdfLecture 4.2: 3D Reconstruction
Lecture 4.2: 3D Reconstruction
Computer Vision
Instructor: Eduard Ramon
dlcv_2018_d4l3_generativemodels.pdfLecture 4.3: Generative models
Lecture 4.3: Generative models
Generative models
Instructor: Kevin McGuinness
dlcv_2018_d4l4_languageandvision.pdfLecture 4.4: Language and Vision
Lecture 4.4: Language and Vision
Multimodal Learning
Instructor: Xavier Giró-i-Nieto
dlcv_2018_d4l5_audioandvision.pdfLecture 4.5: Audio and Vision
Lecture 4.5: Audio and Vision
Multimodal Learning
Instructor: Eva Mohedano
dlcv_2018_d4l6_speechandvision.pdfLecture 4.6: Speech and Vision
Lecture 4.6: Speech and Vision
Multimodal Learning
Instructor: Xavier Giró-i-Nieto
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