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Listen "Ep 28: Unlocking AI Potential: Image Segmentation with U-Net Models"
Episode Synopsis
Description:
Dive into the fascinating world of AI and image segmentation in this episode. We start with the latest AI news, including how OpenAI selected voices for ChatGPT and the implications of the EU's new AI regulations. Discover advancements in AI models with insights on Claude 3 Sonnet and Lumina-T2X, frameworks like Grounding DINO 1.5 for open-set object detection, and multi-view diffusion models for 3D object creation with CAT3D.
Next, we delve into image segmentation techniques, exploring the powerful U-Net, UNet++, and UNet 3+ architectures for medical image segmentation. Learn about thresholding methods and Markov random field models, and key research papers driving innovation in this field.
AI News:
How the voices for ChatGPT were chosen | OpenAI
Artificial intelligence (AI) act: Council gives final green light to the first worldwide rules on AI - Consilium
Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet
Lumina-T2X is a unified framework for Text to Any Modality Generation
Grounding DINO 1.5 Pro
[2405.10300] Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection
[2405.10314] CAT3D: Create Anything in 3D with Multi-View Diffusion Models
[1405.0312] Microsoft COCO: Common Objects in Context
[1908.03195] LVIS: A Dataset for Large Vocabulary Instance Segmentation
AutoQuizzer - a Hugging Face Space by deepset
References for main topic:
[1505.04597] U-Net: Convolutional Networks for Biomedical Image Segmentation
[1807.10165] UNet++: A Nested U-Net Architecture for Medical Image Segmentation
[1706.01805] SegAN: Adversarial Network with Multi-scale $L_1$ Loss for Medical Image Segmentation
[2004.08790] UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation
A Threshold Selection Method from Gray-Level Histograms | IEEE Journals & Magazine
(PDF) Unsupervised Texture Segmentation Using Markov Random Field Models
Unveiling U-Net++: A Hands-On Guide on Image Segmentation | by Alessandro Lamberti | Artificialis | Medium
Dive into the fascinating world of AI and image segmentation in this episode. We start with the latest AI news, including how OpenAI selected voices for ChatGPT and the implications of the EU's new AI regulations. Discover advancements in AI models with insights on Claude 3 Sonnet and Lumina-T2X, frameworks like Grounding DINO 1.5 for open-set object detection, and multi-view diffusion models for 3D object creation with CAT3D.
Next, we delve into image segmentation techniques, exploring the powerful U-Net, UNet++, and UNet 3+ architectures for medical image segmentation. Learn about thresholding methods and Markov random field models, and key research papers driving innovation in this field.
AI News:
How the voices for ChatGPT were chosen | OpenAI
Artificial intelligence (AI) act: Council gives final green light to the first worldwide rules on AI - Consilium
Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet
Lumina-T2X is a unified framework for Text to Any Modality Generation
Grounding DINO 1.5 Pro
[2405.10300] Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection
[2405.10314] CAT3D: Create Anything in 3D with Multi-View Diffusion Models
[1405.0312] Microsoft COCO: Common Objects in Context
[1908.03195] LVIS: A Dataset for Large Vocabulary Instance Segmentation
AutoQuizzer - a Hugging Face Space by deepset
References for main topic:
[1505.04597] U-Net: Convolutional Networks for Biomedical Image Segmentation
[1807.10165] UNet++: A Nested U-Net Architecture for Medical Image Segmentation
[1706.01805] SegAN: Adversarial Network with Multi-scale $L_1$ Loss for Medical Image Segmentation
[2004.08790] UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation
A Threshold Selection Method from Gray-Level Histograms | IEEE Journals & Magazine
(PDF) Unsupervised Texture Segmentation Using Markov Random Field Models
Unveiling U-Net++: A Hands-On Guide on Image Segmentation | by Alessandro Lamberti | Artificialis | Medium
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