18:29

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

24:35

Lecture 20 - OWLv2: Scaling Open-Vocabulary Object Detection

31:20

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

34:56

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

22:49

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

25:59

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

32:28

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

19:57

Presentation - Consistency-Preserving Visual Question Answering in Medical Imaging

23:57

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

14:14

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

17:10

Presentation - Self-Supervised Pre-training for Nuclei Segmentation

21:18

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

22:57

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

16:11

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

21:48

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

25:58

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

35:23

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

19:01

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

22:29

Presentation - Diffusion Models for Medical Anomaly Detection

19:49

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

26:21

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

23:34

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

20:03

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

16:27

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

17:38

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

25:28

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

16:17

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

09:50

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

09:22

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

09:20

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

11:37

Presentation - Hypointensity lesions Segmentation using Attention Mechanism in MRI images

08:42

Presentation - Efficient COVID-19 Classification in Low Resolution Scenarios

07:22

Presentation - Segmentaion of the Left Ventricle

09:42

Presentation - Semi-Supervised Learning for Brain Tumor segmentation

07:35

Presentation - Medical Image Segmentation using token Labeling

13:59

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

11:13

Presentation - Guided Synthesis of MRI Images using C-GAN

05:20

Presentation - Covid-19 Chest X-Ray Classification

09:18

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

07:27

Lecture 16.5 - Instance Segmentation [Mask - RCNN Architecture]

08:04

Lecture 16.3 - Instance Segmentation [RoIAlign vs RoIPool]

05:49

Lecture 16.2 - Instance Segmentation [Mask - RCNN]

05:08

Lecture 16.1 - Instance Segmentation [Introduction to Instance Segmentation]

03:50

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

09:15

Lecture 15.5 - Semantic Segmentation [Upsampling]

06:07

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

04:28

Lecture 15.7 - Semantic Segmentation [U-Net Sampling]

02:54

Lecture 15.2 - Semantic Segmentation [Segmentation tasks]

07:29

Lecture 15.1 - Semantic Segmentation [Introduction to Semantic Segmentation]

07:30

Lecture 15.3 - Semantic Segmentation [Fully Convolutional Networks]

32:42

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

23:48

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

23:46

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

21:58

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

06:47

Lecture 14.14 - Image Segmentation [Mean Shift Clustering]

05:23

Lecture 14.13 - Image Segmentation [Mean Shift Algorithm]

11:15

Lecture 14.5 - Image Segmentation [Region Growing Algorithm]

05:35

Lecture 14.12 - Image Segmentation [Mean Shift Segmentation]

09:29

Lecture 14.11 - Image Segmentation [SLIC Algorithm]

04:51

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