Deep Learning for Computer Vision

Deep Learning for Computer Vision

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_2018_d1l2_theneuralnetworkzoo.pdf

Lecture 1.1: Neural Network Zoo

Learning Paradigms

Instructor: Xavier Giró-i-Nieto

dlcv_2018_d1l3_imageclassification.pdf

Lecture 1.2: Image Classification

Computer Vision

Instructor: Kevin McGuinness

dlcv_2018_d1l4_imageretrieval.pdf

Lecture 1.3: Image Retrieval

Computer Vision

Instructor: Eva Mohedano

dlcv_2018_d1l5_visuallocalization.pdf

Lecture 1.4: Visual Localization

Computer Vision

Instructor: Laura Leal-Taixé

dlcv_2018_d1l6_videosegmentation.pdf

Lecture 1.5: Video Object Segmentation

Computer Vision

Instructor: Laura Leal Taixé

dlcv_2018_d2l1_objectdetection.pdf

Lecture 2.1: Object Detection

Computer Vision

Instructor: Miriam Bellver

dlcv_2018_d2l2_facerecognition.pdf

Lecture 2.2: Face Detection & Recognition

Computer Vision

Instructor: Elisa Sayrol

dlcv_2018_d2l3_semanticsegmentation.pdf

Lecture 2.3: Semantic Segmentation

Computer Vision

Instructor: Miriam Bellver

dlcv_2018_d2l4_instancesegmentation.pdf

Lecture 2.4: Instance Segmentation

Computer Vision

Instructor: Miriam Bellver

dlcv_2018_d2l5_medicalimaging.pdf

Lecture 2.5: Medical Imaging

Computer Vision

Instructor: Elisa Sayrol

dlcv_2018_d3l1_2_videoanalysis.pdf

Lecture 3.1 & 3.2: Video Analysis

Computer Vision

Instructor: Victor Campos

dlcv_2018_d3l3_objecttracking.pdf

Lecture 3.3: Object Tracking

Computer Vision

Instructor: Laura Leal Taixé

dlcv_2018_d3l4_interpretability.pdf

Lecture 3.4: Interpretability

Computer Vision

Instructor: Eva Mohedano

dlcv_2018_d3l5_saliencyprediction.pdf

Lecture 3.5: Saliency Prediction

Computer Vision

Instructor: Kevin McGuinness

dlcv_2018_d3l6_setlearning.pdf

Lecture 3.6: Set Learning

Computer Vision

Instructor: Laura Leal Taixé

dlcv_2018_d4l1_3danalysis.pdf

Lecture 4.1: 3D Analysis

Computer Vision

Instructor: Javier Ruiz

dlcv_2018_d4l2_3dreconstruction.pdf

Lecture 4.2: 3D Reconstruction

Computer Vision

Instructor: Eduard Ramon

dlcv_2018_d4l3_generativemodels.pdf

Lecture 4.3: Generative models

Generative models

Instructor: Kevin McGuinness

dlcv_2018_d4l4_languageandvision.pdf

Lecture 4.4: Language and Vision

Multimodal Learning

Instructor: Xavier Giró-i-Nieto

dlcv_2018_d4l5_audioandvision.pdf

Lecture 4.5: Audio and Vision

Multimodal Learning

Instructor: Eva Mohedano

dlcv_2018_d4l6_speechandvision.pdf

Lecture 4.6: Speech and Vision

Multimodal Learning

Instructor: Xavier Giró-i-Nieto