Канал: UCF CRCV

P. Tirupattur-Video Action Understanding: Action Classification, Temporal Localization and Detection

P. Tirupattur-Video Action Understanding: Action Classification, Temporal Localization and Detection

Lecture 22 - FuseCap: Leveraging Large Language Models for Enriched Fused Image Captions

Lecture 22 - FuseCap: Leveraging Large Language Models for Enriched Fused Image Captions

Lecture 20 - OWLv2: Scaling Open-Vocabulary Object Detection

Lecture 20 - OWLv2: Scaling Open-Vocabulary Object Detection

Lecture 21 - Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

Lecture 21 - Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

Lecture 19 - CM3Leon: Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning

Lecture 19 - CM3Leon: Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning

Presentation - Adapting Pretrained Vision Language Foundational Models to Medical Imaging Domains

Presentation - Adapting Pretrained Vision Language Foundational Models to Medical Imaging Domains

Presentation - Benchmarking the Robustness of Deep Neural Networks to Common Corruptions in Digital

Presentation - Benchmarking the Robustness of Deep Neural Networks to Common Corruptions in Digital

Presentation - FIBA - Frequency Injection based Backdoor Attack in Medical Image Analysis

Presentation - FIBA - Frequency Injection based Backdoor Attack in Medical Image Analysis

Presentation - Consistency-Preserving Visual Question Answering in Medical Imaging

Presentation - Consistency-Preserving Visual Question Answering in Medical Imaging

Presentation - MedKLIP - Medical Knowledge Enhanced Language-Image Pre -Training

Presentation - MedKLIP - Medical Knowledge Enhanced Language-Image Pre -Training

Presentation - Intra-class Contrastive Learning Improves Computer Aided Diagnosis of Breast Cancer

Presentation - Intra-class Contrastive Learning Improves Computer Aided Diagnosis of Breast Cancer

Presentation - Self-Supervised Pre-training for Nuclei Segmentation

Presentation - Self-Supervised Pre-training for Nuclei Segmentation

Presentation - Is PET all you need - A multi-modal study for Alzheimer's disease using 3D CNNs

Presentation - Is PET all you need - A multi-modal study for Alzheimer's disease using 3D CNNs

Presentation - Real-Time Monitoring of User Stress, Heart Rate, and Heart Rate Variability on Mobile

Presentation - Real-Time Monitoring of User Stress, Heart Rate, and Heart Rate Variability on Mobile

Presentation - CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection

Presentation - CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection

Presentation - A controllable and Simultaneous Synthesizer of Images and Annotations with Minimal Hu

Presentation - A controllable and Simultaneous Synthesizer of Images and Annotations with Minimal Hu

Presentation - Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentation

Presentation - Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentation

Presentation - A Robust Volumetric Transformer for Accurate 3D Tumor Segmentation

Presentation - A Robust Volumetric Transformer for Accurate 3D Tumor Segmentation

Presentation - Self-Supervised Pre-Training of Swim Transformers for 3D Medical Image Analysis

Presentation - Self-Supervised Pre-Training of Swim Transformers for 3D Medical Image Analysis

Presentation - Diffusion Models for Medical Anomaly Detection

Presentation - Diffusion Models for Medical Anomaly Detection

Presentation - DiRA -Discriminative, Restorative, and Adversarial Learning for Self supervised Medic

Presentation - DiRA -Discriminative, Restorative, and Adversarial Learning for Self supervised Medic

Presentation - Scribble 2D5 -Weakly Supervised Volumetric Image Segmentation via scribble Annotation

Presentation - Scribble 2D5 -Weakly Supervised Volumetric Image Segmentation via scribble Annotation

Presentation - Rethinking Breast Lesion Segmentation in Ultrasound - New Video Dataset and Baseline

Presentation - Rethinking Breast Lesion Segmentation in Ultrasound - New Video Dataset and Baseline

Presentation - UNETR++ Delving into Efficient and Accurate 3D Medical Image Segmentation

Presentation - UNETR++ Delving into Efficient and Accurate 3D Medical Image Segmentation

Presentation - BabyNet Residual Transformer Module for Birth Weight Prediction Fetal Ultrasound Vi

Presentation - BabyNet Residual Transformer Module for Birth Weight Prediction Fetal Ultrasound Vi

Presentation - Pose-based Tremor Classification for Parkinson's Disease Diagnosis from Video

Presentation - Pose-based Tremor Classification for Parkinson's Disease Diagnosis from Video

Presentation - mmFormer Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain T

Presentation - mmFormer Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain T

Presentation - GaitForeMer Self Supervised Pretraining of Transformers via Human Motion Forecasting

Presentation - GaitForeMer Self Supervised Pretraining of Transformers via Human Motion Forecasting

Presentation - Malaria Cell Stage Classification using Knowledge Distillation & Relational Learning

Presentation - Malaria Cell Stage Classification using Knowledge Distillation & Relational Learning

Presentation - Medicla Image Synthesis for Data Augmentation and Anonymization via Diffusion

Presentation - Medicla Image Synthesis for Data Augmentation and Anonymization via Diffusion

Presentation-Transfer Learning in Medical Images-Exploring Impact on Model Accuracy of Breast Tumor

Presentation-Transfer Learning in Medical Images-Exploring Impact on Model Accuracy of Breast Tumor

Presentation - Hypointensity lesions Segmentation using Attention Mechanism in MRI images

Presentation - Hypointensity lesions Segmentation using Attention Mechanism in MRI images

Presentation - Efficient COVID-19 Classification in Low Resolution Scenarios

Presentation - Efficient COVID-19 Classification in Low Resolution Scenarios

Presentation - Segmentaion of the Left Ventricle

Presentation - Segmentaion of the Left Ventricle

Presentation - Semi-Supervised Learning for Brain Tumor segmentation

Presentation - Semi-Supervised Learning for Brain Tumor segmentation

Presentation - Medical Image Segmentation using token Labeling

Presentation - Medical Image Segmentation using token Labeling

Presentation - Low-Cost Detection of Malaria Parasites form Microscopic Images

Presentation - Low-Cost Detection of Malaria Parasites form Microscopic Images

Presentation - Guided Synthesis of MRI Images using C-GAN

Presentation - Guided Synthesis of MRI Images using C-GAN

Presentation - Covid-19 Chest X-Ray Classification

Presentation - Covid-19 Chest X-Ray Classification

Lecture 16.4 - Instance Segmentation [RoI-Align vs RoI-Pooling Example]

Lecture 16.4 - Instance Segmentation [RoI-Align vs RoI-Pooling Example]

Lecture 16.5 - Instance Segmentation [Mask - RCNN Architecture]

Lecture 16.5 - Instance Segmentation [Mask - RCNN Architecture]

Lecture 16.3 - Instance Segmentation [RoIAlign vs RoIPool]

Lecture 16.3 - Instance Segmentation [RoIAlign vs RoIPool]

Lecture 16.2 - Instance Segmentation [Mask - RCNN]

Lecture 16.2 - Instance Segmentation [Mask - RCNN]

Lecture 16.1 - Instance Segmentation [Introduction to Instance Segmentation]

Lecture 16.1 - Instance Segmentation [Introduction to Instance Segmentation]

Lecture 15.6 - Semantic Segmentation [Deconvolution network for Semantic Segmentation]

Lecture 15.6 - Semantic Segmentation [Deconvolution network for Semantic Segmentation]

Lecture 15.5 - Semantic Segmentation [Upsampling]

Lecture 15.5 - Semantic Segmentation [Upsampling]

Lecture 15.4 - Semantic Segmentation [Skip Connections in Fully Convolutional Networks]

Lecture 15.4 - Semantic Segmentation [Skip Connections in Fully Convolutional Networks]

Lecture 15.7 - Semantic Segmentation [U-Net Sampling]

Lecture 15.7 - Semantic Segmentation [U-Net Sampling]

Lecture 15.2 - Semantic Segmentation [Segmentation tasks]

Lecture 15.2 - Semantic Segmentation [Segmentation tasks]

Lecture 15.1 - Semantic Segmentation [Introduction to Semantic Segmentation]

Lecture 15.1 - Semantic Segmentation [Introduction to Semantic Segmentation]

Lecture 15.3 - Semantic Segmentation [Fully Convolutional Networks]

Lecture 15.3 - Semantic Segmentation [Fully Convolutional Networks]

Lecture 18 - Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs

Lecture 18 - Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs

Lecture 17 - Evaluating Object Hallucination in Large Vision-Language Models

Lecture 17 - Evaluating Object Hallucination in Large Vision-Language Models

Lecture 16 - PG-Video-LLaVA: Pixel Grounding Large Video-Language Models

Lecture 16 - PG-Video-LLaVA: Pixel Grounding Large Video-Language Models

Lecture 15 - Video-LLaVA: Learning United Visual Representation by Alignment Before Projection

Lecture 15 - Video-LLaVA: Learning United Visual Representation by Alignment Before Projection

Lecture 14.14 - Image Segmentation [Mean Shift Clustering]

Lecture 14.14 - Image Segmentation [Mean Shift Clustering]

Lecture 14.13 - Image Segmentation [Mean Shift Algorithm]

Lecture 14.13 - Image Segmentation [Mean Shift Algorithm]

Lecture 14.5 - Image Segmentation [Region Growing Algorithm]

Lecture 14.5 - Image Segmentation [Region Growing Algorithm]

Lecture 14.12 - Image Segmentation [Mean Shift Segmentation]

Lecture 14.12 - Image Segmentation [Mean Shift Segmentation]

Lecture 14.11 - Image Segmentation [SLIC Algorithm]

Lecture 14.11 - Image Segmentation [SLIC Algorithm]

Lecture 14.10 - Image Segmentation [K-means Loss Function]

Lecture 14.10 - Image Segmentation [K-means Loss Function]

Lecture 14.4 - Image Segmentation [Otsu Example]

Lecture 14.4 - Image Segmentation [Otsu Example]

Lecture 14.9 - Image Segmentation [K-means clustering algorithm]

Lecture 14.9 - Image Segmentation [K-means clustering algorithm]

Lecture 14.8 - Image Segmentation [Natural Grouping and Distance Metrics]

Lecture 14.8 - Image Segmentation [Natural Grouping and Distance Metrics]

Lecture 13.12 - Object Detection [CNN based Approach RCNN]

Lecture 13.12 - Object Detection [CNN based Approach RCNN]

Lecture 14.7 - Image Segmentation [Introduction to Clustering]

Lecture 14.7 - Image Segmentation [Introduction to Clustering]

Lecture 14.6 - Image Segmentation [Region Splitting and Merging 1]

Lecture 14.6 - Image Segmentation [Region Splitting and Merging 1]

Lecture 14.3 - Image Segmentation [Thresholding Examples]

Lecture 14.3 - Image Segmentation [Thresholding Examples]

Lecture 14.2 - Image Segmentation [Image Binarization]

Lecture 14.2 - Image Segmentation [Image Binarization]

Lecture 13.11 - Object Detection [CNN Based Approach - A Simple Solution]

Lecture 13.11 - Object Detection [CNN Based Approach - A Simple Solution]

Lecture 13.13 - Object Detection [CNN based Approach Fast R-CNN]

Lecture 13.13 - Object Detection [CNN based Approach Fast R-CNN]

Lecture 13.14 - Object Detection [Per class or class agnostic regression]

Lecture 13.14 - Object Detection [Per class or class agnostic regression]

Lecture 16 - Instance Segmentation

Lecture 16 - Instance Segmentation

Lecture 13.10 - Object Detection [Mean Average Precision]

Lecture 13.10 - Object Detection [Mean Average Precision]

Lecture 13.9 - Object Detection [Precision and Recall]

Lecture 13.9 - Object Detection [Precision and Recall]

Lecture 13.8 - Object Detection [Evaluation using IOU]

Lecture 13.8 - Object Detection [Evaluation using IOU]

Lecture 13.7 - Object Detection [Non maximum suppression]

Lecture 13.7 - Object Detection [Non maximum suppression]

Lecture 13.6 - Object Detection [Extract Features, Classify Features, Post Processing]

Lecture 13.6 - Object Detection [Extract Features, Classify Features, Post Processing]

Lecture 13.5 - Object Detection [Gaussian Pyramid Construction and Challenges]

Lecture 13.5 - Object Detection [Gaussian Pyramid Construction and Challenges]

Lecture 13.4 - Object Detection [Gaussian pre-filtering]

Lecture 13.4 - Object Detection [Gaussian pre-filtering]

Lecture 13.3 - Object Detection [Example of Sliding Window Approach]

Lecture 13.3 - Object Detection [Example of Sliding Window Approach]

Lecture 13.2 - Object Detection [Introduction to Sliding Window, Template Matching]

Lecture 13.2 - Object Detection [Introduction to Sliding Window, Template Matching]

Lecture 13.1 - Object Detection {Introduction to Object Detection]

Lecture 13.1 - Object Detection {Introduction to Object Detection]

Lecture 12.7 - Classification II [Loss Function, Visualizing Convolution]

Lecture 12.7 - Classification II [Loss Function, Visualizing Convolution]

Lecture 12.6 - Classification II [Softmax Classification]

Lecture 12.6 - Classification II [Softmax Classification]

Lecture 12.5 - Classification II [Activation Functions]

Lecture 12.5 - Classification II [Activation Functions]

Lecture 12.4 - Classification II [Introducing Non linearities]

Lecture 12.4 - Classification II [Introducing Non linearities]

Lecture 12.3 - Classification II [Converting Fully Convolution to Convolution]

Lecture 12.3 - Classification II [Converting Fully Convolution to Convolution]

Lecture 12.2 - Classification II [Fully Convolutional Network]

Lecture 12.2 - Classification II [Fully Convolutional Network]

Lecture 12.1 - Classification II [Classification I Recap]

Lecture 12.1 - Classification II [Classification I Recap]

Lecture 11.15 - Classification I [Multi class SVM]

Lecture 11.15 - Classification I [Multi class SVM]

Lecture 11.14 - Classification I [Non linear SVM]

Lecture 11.14 - Classification I [Non linear SVM]

Lecture 11.13 - Classification I [Advantages of non linear boundary]

Lecture 11.13 - Classification I [Advantages of non linear boundary]

Lecture 11.12 - Classification I [Disadvantages of linear boundary]

Lecture 11.12 - Classification I [Disadvantages of linear boundary]

Lecture 11.11 - Classification I [Support Vector Classifier Example]

Lecture 11.11 - Classification I [Support Vector Classifier Example]

Lecture 11.10 - Classification I [Support Vector Classifier - Soft Margin]

Lecture 11.10 - Classification I [Support Vector Classifier - Soft Margin]

Lecture 11.9 - Classification I [Support Vector Classifier - Separable case]

Lecture 11.9 - Classification I [Support Vector Classifier - Separable case]

Lecture 11.8 - Classification I [Understanding Non separable case]

Lecture 11.8 - Classification I [Understanding Non separable case]

Lecture 11.7 - Classification I [Maximum Margin Classifier - SVM]

Lecture 11.7 - Classification I [Maximum Margin Classifier - SVM]

Lecture 11.6 - Classification I [K-Nearest Neighbor]

Lecture 11.6 - Classification I [K-Nearest Neighbor]

Lecture 11.5 - Classification I [Nearest Neighbor Classifier]

Lecture 11.5 - Classification I [Nearest Neighbor Classifier]

Lecture 11.4 - Classification I [General Framework, Decision Boundaries for classification]

Lecture 11.4 - Classification I [General Framework, Decision Boundaries for classification]

Lecture 11.3 - Classification I [Understanding how to Perform classification for the Given Data]

Lecture 11.3 - Classification I [Understanding how to Perform classification for the Given Data]

Lecture 11.2 - Classification I [Types of classification]

Lecture 11.2 - Classification I [Types of classification]

Lecture 11.1 Classification I [Introduction to classification]

Lecture 11.1 Classification I [Introduction to classification]

Lecture 8.9 - PyTorch Tutorial [Defining a Network class part 2]

Lecture 8.9 - PyTorch Tutorial [Defining a Network class part 2]

Lecture 8.15 - PyTorch Tutorial [Update Parameters]

Lecture 8.15 - PyTorch Tutorial [Update Parameters]

Lecture 8.13 - PyTorch Tutorial [Loss Function]

Lecture 8.13 - PyTorch Tutorial [Loss Function]

Lecture 8.14 - PyTorch Tutorial [Gradient Computation]

Lecture 8.14 - PyTorch Tutorial [Gradient Computation]

Lecture 8.12 - PyTorch Tutorial [Process input through the Network]

Lecture 8.12 - PyTorch Tutorial [Process input through the Network]

Lecture 8.11 - PyTorch Tutorial [Iterate over a Dataset of Inputs]

Lecture 8.11 - PyTorch Tutorial [Iterate over a Dataset of Inputs]

Lecture 8.10 - PyTorch Tutorial [Defining a CNN Network - Example]

Lecture 8.10 - PyTorch Tutorial [Defining a CNN Network - Example]

Lecture 8.8 - PyTorch Tutorial [Defining a Network class]

Lecture 8.8 - PyTorch Tutorial [Defining a Network class]

Lecture 8.7 - PyTorch Tutorial [Building Neural Network]

Lecture 8.7 - PyTorch Tutorial [Building Neural Network]

Lecture 8.6 - PyTorch Tutorial [Training Procedure]

Lecture 8.6 - PyTorch Tutorial [Training Procedure]

Lecture 8.5 - PyTorch Tutorial [Computational Graphs and Automatic Gradient Computation]

Lecture 8.5 - PyTorch Tutorial [Computational Graphs and Automatic Gradient Computation]

Lecture 8.4 - PyTorch Tutorial [Matrix Multiplication in PyTorch]

Lecture 8.4 - PyTorch Tutorial [Matrix Multiplication in PyTorch]

Lecture 8.3 - PyTorch Tutorial [Torch Tensor vs Numpy Array]

Lecture 8.3 - PyTorch Tutorial [Torch Tensor vs Numpy Array]

Lecture 8.2 - PyTorch Tutorial [PyTorch Operations]

Lecture 8.2 - PyTorch Tutorial [PyTorch Operations]

Lecture 8.1 - PyTorch Tutorial [PyTorch Tensor]

Lecture 8.1 - PyTorch Tutorial [PyTorch Tensor]

Lecture 10.5 - Autoencoders [Applications, and Properties of Autoencoder]

Lecture 10.5 - Autoencoders [Applications, and Properties of Autoencoder]

Lecture 10.4 - Autoencoders [Feature Learning using Autoencoder]

Lecture 10.4 - Autoencoders [Feature Learning using Autoencoder]

Lecture 10.3 - Autoencoders [How Autoencoders work - Examples]

Lecture 10.3 - Autoencoders [How Autoencoders work - Examples]

Lecture 10.2 - Autoencoders [Introduction to Autoencoder - CNN]

Lecture 10.2 - Autoencoders [Introduction to Autoencoder - CNN]

Lecture 10.1 - Autoencoders [Introduction to Autoencoder]

Lecture 10.1 - Autoencoders [Introduction to Autoencoder]

Lecture 9.9 - Features [Corner Detection by Auto correlation]

Lecture 9.9 - Features [Corner Detection by Auto correlation]

Lecture 9.8 - Features [Corner Detection]

Lecture 9.8 - Features [Corner Detection]

Lecture 9.7 - Features [Properties of Interest Points]

Lecture 9.7 - Features [Properties of Interest Points]

Lecture 9.6 - Features [Key Points]

Lecture 9.6 - Features [Key Points]

Lecture 9.5 - Features [Characteristics of good Features]

Lecture 9.5 - Features [Characteristics of good Features]

Lecture 9.15 - Features [Histogram of Oriented Gradients Example]

Lecture 9.15 - Features [Histogram of Oriented Gradients Example]

Lecture 9.14 - Features [Histogram of Oriented Gradients]

Lecture 9.14 - Features [Histogram of Oriented Gradients]

Lecture 9.13 - Features [HOG - Human Detection]

Lecture 9.13 - Features [HOG - Human Detection]

Lecture 9.12 - Features [Histogram of Gradients Edges]

Lecture 9.12 - Features [Histogram of Gradients Edges]

Lecture 9.11 - Features [Harris Corner Detection]

Lecture 9.11 - Features [Harris Corner Detection]

Lecture 9.10 - Features [Corner Detection strategy]

Lecture 9.10 - Features [Corner Detection strategy]

Lecture 9.28 - Features [Summary of SIFT Descriptor]

Lecture 9.28 - Features [Summary of SIFT Descriptor]

Lecture 9.27 - Features [Understanding formation of local features in window]

Lecture 9.27 - Features [Understanding formation of local features in window]

Lecture 9.26 - Features [Normalization of Orientation]

Lecture 9.26 - Features [Normalization of Orientation]

Lecture 9.25 - Features Extracting Feature Orientation

Lecture 9.25 - Features Extracting Feature Orientation

Lecture 9.24 - Features [Implementing Difference of Gaussian]

Lecture 9.24 - Features [Implementing Difference of Gaussian]

Lecture 9.23 - Features [SIFT Recap]

Lecture 9.23 - Features [SIFT Recap]

Lecture 9.22 - Features [Optimizing LoG filter]

Lecture 9.22 - Features [Optimizing LoG filter]

Lecture 9.21 - Features [Finding local maxima in given image]

Lecture 9.21 - Features [Finding local maxima in given image]

Lecture 9.20 - Features [Understanding of useful signature function]

Lecture 9.20 - Features [Understanding of useful signature function]

Lecture 9.19 - Features [Automatic scale selection]

Lecture 9.19 - Features [Automatic scale selection]

Lecture 9.18 - Features [Overall Procedure of SIFT at a High Level]

Lecture 9.18 - Features [Overall Procedure of SIFT at a High Level]

Lecture 9.17 - Features [Introduction to SIFT with an Example]

Lecture 9.17 - Features [Introduction to SIFT with an Example]

Lecture 11.16 - Classification I [Pros and Cons of SVM]

Lecture 11.16 - Classification I [Pros and Cons of SVM]

Lecture 9.16 - Features [Summary of HOG Computation]

Lecture 9.16 - Features [Summary of HOG Computation]

Lecture 9.4 - Features [Finding Features in videos]

Lecture 9.4 - Features [Finding Features in videos]

Lecture 9.3 - Features [Uses of Features]

Lecture 9.3 - Features [Uses of Features]

Lecture 9.2 - Features [Types of Features]

Lecture 9.2 - Features [Types of Features]

Lecture 9.1 - Features [Introduction to Features]

Lecture 9.1 - Features [Introduction to Features]

Lecture 8.16 - PyTorch Tutorial [Model Training - Full Procedure]

Lecture 8.16 - PyTorch Tutorial [Model Training - Full Procedure]

Lecture 14 - Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic

Lecture 14 - Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic

Lecture 4 - Visual-Language Models Introduction Part-I: CoCA, PALI

Lecture 4 - Visual-Language Models Introduction Part-I: CoCA, PALI

Lecture 3 - CLIP

Lecture 3 - CLIP

Lecture 2 - Transformers Introduction

Lecture 2 - Transformers Introduction

Lecture 1 - Introduction

Lecture 1 - Introduction

Lecture 5 - Visual-Language Models Introduction Part-II: FLAMINGO, FLAVA, PAINTER, BLIP-2

Lecture 5 - Visual-Language Models Introduction Part-II: FLAMINGO, FLAVA, PAINTER, BLIP-2

Lecture 6 - Visual-Language Models Introduction Part-III: Image-Bind, Language-Bind, LLaVA

Lecture 6 - Visual-Language Models Introduction Part-III: Image-Bind, Language-Bind, LLaVA

Lecture 7 - Visual-Language Models Introduction Part-IV: Video ChatGPT, PG-Video LLaVA

Lecture 7 - Visual-Language Models Introduction Part-IV: Video ChatGPT, PG-Video LLaVA

Lecture 13 - MERLOT RESERVE: Neural Script Knowledge through Vision and Language and Sound

Lecture 13 - MERLOT RESERVE: Neural Script Knowledge through Vision and Language and Sound

Lecture 12 - MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks

Lecture 12 - MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks

Lecture 15 - Semantic Segmentation

Lecture 15 - Semantic Segmentation

Lecture 3 - Image Filtering

Lecture 3 - Image Filtering

Lecture 14 - Image Segmentation

Lecture 14 - Image Segmentation

Lecture 9 - Features

Lecture 9 - Features

Lecture 6 - Introduction to Convolutional Neural Networks

Lecture 6 - Introduction to Convolutional Neural Networks

Lecture 7 - Training Neural Networks

Lecture 7 - Training Neural Networks

Lecture 8 - PyTorch Tutorial

Lecture 8 - PyTorch Tutorial

Lecture 5 - Introduction to Neural Networks

Lecture 5 - Introduction to Neural Networks

Lecture 13 - Object Detection

Lecture 13 - Object Detection

Lecture 12 - Classification II

Lecture 12 - Classification II

Lecture 11 - Classification I

Lecture 11 - Classification I

Lecture 10 - Autoencoder

Lecture 10 - Autoencoder

Lecture 4 - Edge Detection

Lecture 4 - Edge Detection

Lecture 2 - Linear Algebra Basics

Lecture 2 - Linear Algebra Basics

Lecture 1 - Introduction to Computer Vision

Lecture 1 - Introduction to Computer Vision

Lecture 7.19 - Training Neural Networks [DenseNet]

Lecture 7.19 - Training Neural Networks [DenseNet]

Lecture 7.18 - Training Neural Networks [GoogleNet Inception]

Lecture 7.18 - Training Neural Networks [GoogleNet Inception]

Lecture 7.17 - Training Neural Networks [Residual Network]

Lecture 7.17 - Training Neural Networks [Residual Network]

Lecture 7.16 - Training Neural Networks [AlexNet Training]

Lecture 7.16 - Training Neural Networks [AlexNet Training]

Lecture 7.15 - Training Neural Networks [Training Steps]

Lecture 7.15 - Training Neural Networks [Training Steps]

Lecture 7.14 - Training Neural Networks Dropout

Lecture 7.14 - Training Neural Networks Dropout

Lecture 7.13 - Training Neural Networks [Regularization Ensemble]

Lecture 7.13 - Training Neural Networks [Regularization Ensemble]

Lecture 7.12 - Training Neural Networks [Regularization]

Lecture 7.12 - Training Neural Networks [Regularization]

Lecture 7.11 - Training Neural Networks [Lowering Learning Rate]

Lecture 7.11 - Training Neural Networks [Lowering Learning Rate]

Lecture 7.10 - Training Neural Networks [Data Fitting Problem]

Lecture 7.10 - Training Neural Networks [Data Fitting Problem]

Lecture 7.9 - Training Neural Networks [Gradient Descent Oscillations]

Lecture 7.9 - Training Neural Networks [Gradient Descent Oscillations]

Lecture 7.8 - Training Neural Networks [Stochastic Gradient Descent - Part 2]

Lecture 7.8 - Training Neural Networks [Stochastic Gradient Descent - Part 2]

Lecture 7.7 - Training Neural Networks [Stochastic Gradient Descent]

Lecture 7.7 - Training Neural Networks [Stochastic Gradient Descent]

Lecture 7.6 - Training Neural Networks [Backpropagation Chain Rule]

Lecture 7.6 - Training Neural Networks [Backpropagation Chain Rule]

Lecture 7.5 - Training Neural Networks [Differentiability in Neural Networks]

Lecture 7.5 - Training Neural Networks [Differentiability in Neural Networks]

Lecture 7.4 - Training Neural Networks [Train CNN with Gradient Descent]

Lecture 7.4 - Training Neural Networks [Train CNN with Gradient Descent]

Lecture 7.3 - Training Neural Networks [Gradient Descent in Neural Networks]

Lecture 7.3 - Training Neural Networks [Gradient Descent in Neural Networks]

Lecture 7.2 Training Neural Networks [Loss Function]

Lecture 7.2 Training Neural Networks [Loss Function]

Lecture 7.1 - Training Neural Networks [Basics Recap]

Lecture 7.1 - Training Neural Networks [Basics Recap]

Lecture 10-BLIP:Bootstrapping Language-Image Pretraining for Unified VL Understanding and Generation

Lecture 10-BLIP:Bootstrapping Language-Image Pretraining for Unified VL Understanding and Generation