Want to jump ahead in artificial intelligence and/or digital pathology? Excited to share that after 2+ years of development PathML 2.0 is out! An open source #computational #pathology software library created by Dana-Farber Cancer Institute/Harvard Medical School and Weill Cornell Medicine led by Massimo Loda to lower the barrier to entry to #digitalpathology and #artificialintelligence , and streamline all #imageanalysis or #deeplearning workflows.

⭐ Code: https://github.com/Dana-Farber-AIOS/pathml

🆔🆔 Magic-Me: Identity-Specific Video 🆔🆔

👉hashtag#ByteDance (+UC Berkeley) unveils VCD for video-gen: with just a few images of a specific identity it can generate temporal consistent videos aligned with the given prompt. Impressive results, source code under Apache 2.0 💙

𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Novel Video Custom Diffusion (VCD) framework
✅High-Quality ID-specific videos generation
✅Improvement in aligning IDs-images and text
✅Robust 3D Gaussian Noise Prior for denoising
✅Better Inter-frame correlation / video consistency
✅New modules F-VCD/T-VCD for videos upscale
✅New train with masked loss by prompt-to-segmentation

hashtag#artificialintelligence hashtag#machinelearning hashtag#ml hashtag#AI hashtag#deeplearning hashtag#computervision hashtag#AIwithPapers hashtag#metaverse

👉Channel: @deeplearning_ai
👉Paper https://arxiv.org/pdf/2402.09368.pdf
👉Project https://magic-me-webpage.github.io/
👉Code https://github.com/Zhen-Dong/Magic-Me

Introducing ECoDepth: The New Benchmark in Diffusive Mono-Depth

From the labs of IITD, we unveil ECoDepth - our groundbreaking SIDE model powered by a diffusion backbone and enriched with ViT embeddings. This innovation sets a new standard in single image depth estimation (SIDE), offering unprecedented accuracy and semantic understanding.

Key Features:

✅Revolutionary MDE approach tailored for SIDE tasks
✅Enhanced semantic context via ViT embeddings
✅Superior performance in zero-shot transfer tasks
✅Surpasses previous SOTA models by up to 14%

Dive into the future of depth estimation with ECoDepth. Access our source code and explore the full potential of our model.

📖 Read the Paper
💻 Get the Code

#ArtificialIntelligence #MachineLearning #DeepLearning #ComputerVision #AIwithPapers #Metaverse

join our community:
👉 @deeplearning_ai

Neural Bodies with Clothes: Overview

Introduction: Neural-ABC, a cutting-edge parametric model developed by the University of Science & Technology of China, innovatively represents clothed human bodies.

Key Features:

✅Novel approach for modeling clothed human figures.
✅Unified framework accommodating various clothing types.
✅Consistent representation of both body and clothing.
✅Enables seamless modification of identity, shape, clothing, and pose.
✅Extensive dataset with detailed clothing information.

Explore More:
💻Project Details: Discover More
📖Read the Paper: Access Here
💻Source Code: Explore on GitHub

Relevance: #artificialintelligence #machinelearning #AI #deeplearning #computervision

join our community:
👉 @deeplearning_ai

🚀 Explore SCRFD: High-Efficiency, High-Accuracy Face Detection 🚀


Unlock next-level face detection capabilities with SCRFD – efficiency and accuracy in one solution!


📈 Performance at a Glance:

✅Model range: SCRFD_500M to SCRFD_34G
✅Accuracy up to 96.06%
✅Inference as fast as 3.6 ms

🔍 Explore more and consider starring our repo for updates:
--- GitHub Repository.
--- Paper



#AI #MachineLearning #FaceDetection #TechInnovation #DeepLearning

✅ https://t.me/deeplearning_ai

🔍 Discover the Power of Fine-Grained Gaze Estimation with L2CS-Net! 🌟

🚀 Key Features:
✅Advanced Architecture: Built using state-of-the-art neural network structures.
✅Versatile Utilities: Packed with utility functions and classes for seamless integration.
✅Robust Data Handling: Efficient data loading, preprocessing, and augmentation.
✅Comprehensive Training & Testing: Easy-to-follow scripts for training and testing your models.

👀 Live Demo:
Visualize the power of L2CS-Net with your own video:


🌟 Join Us:
Star our repo on GitHub and be part of the innovative community pushing the boundaries of gaze estimation. Your support drives us forward!

🔗 GitHub Repository

Let's advance gaze estimation together! 🚀🌐 #GazeEstimation #DeepLearning #AI #MachineLearning #ComputerVision

🌟 Exciting AI Breakthrough! Meet U^2-Net! 🌟

🌟 Why U^2-Net?
* Efficient
* AdvancedArchitecture.
* High-Resolution Outputs
🚀 Key Applications:
* Salient Object Detection
* Background Removal
* Medical Imaging

💡 Ready to Transform Projects

✨ Give our repo a ⭐ and show your support!

#AI #DeepLearning #U2Net #ImageSegmentation #OpenSource #GitHub

💻Source Code: Explore GitHub Repo

Happy Learning! 🌟

🚀 3DGazeNet: Revolutionizing Gaze Estimation with Weak-Supervision! 🌟

Key Features:
🔹 Advanced Neural Network: Built on the robust U2-Net architecture.
🔹 Comprehensive Utilities: Easy data loading, preprocessing, and augmentation.
🔹 Seamless Integration: Train, test, and visualize with simple commands.

Demo Visualization:Visualize the demo by configuring your video path in main.py and showcasing the power of 3DGazeNet.

Pretrained Weights:Quick start with our pretrained weights stored in the weights folder.

💻Source Code: https://github.com/Shohruh72/3DGazeNet
📖Read the Paper: Access Here


#3DGazeNet #GazeEstimation #AI #DeepLearning #TechInnovation

Join us in pushing the boundaries of gaze estimation technology with 3DGazeNet!

🚀 Introducing L2CS-Net: Fine-Grained Gaze Estimation 👀✨

🔗 GitHub Repo: Star ⭐ the Repo

🔥 Key Features:
✅ Fine-grained gaze estimation with deep learning
✅ Supports Gaze360 dataset
✅ Train with Single-GPU / Multi-GPU
✅ Demo for real-time visualization

📌 Quick Start:
🗂️ Prepare dataset
🏋️ Train (python main.py --train)
🎥 Video Infernece (python main.py --demo)

🌟 Support Open Source! Star ⭐ & Share!
🔗 GitHub Repo: L2CSNet

#AI #DeepLearning #GazeEstimation #L2CSNet #OpenSource 🚀

🚀 Refactored HRNet Now Live! 🚀

🔥 Supercharge your computer vision projects with high-resolution HRNet models – fully refactored for easy training/testing!

✅ Multiple ImageNet-pretrained models
✅ Lightning-fast setup
✅ Top-tier accuracy

👉 Check it out & ⭐️ Star the repo if you find it useful!
GitHub: Shohruh72/HRNet

#AI #DeepLearning #OpenSource

@deeplearning_ai

🚀 3DGazeNet: Next-Gen Gaze Estimation! - get instant results with just one click.

Discover how to train powerful gaze estimation models using only synthetic data and weak supervision—no huge real-world datasets needed.
Perfect for AR/VR, HCI, and beyond.
Cutting-edge, open-source, and ready for your next project!

👉 Try it now: https://github.com/Shohruh72/3DGazeNet

#DeepLearning #GazeEstimation #AI

@deeplearning_ai