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EfficientViT - SAM:69x Faster SAM: Multi-Scale Linear Attention for High-Resolution Dense Prediction

1. Channel: @deeplearning_ai

2.Source Code: https://github.com/mit-han-lab/efficientvit

3. Paper: https://arxiv.org/abs/2402.05008

🆔🆔 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

🌟 Discover 6DRepNet: The Ultimate Head Pose Estimation Model!

Features:
* State-of-the-art accuracy
* Comprehensive tools for training, testing, and inference
* Easy setup with conda
* Supports multiple datasets

Watch the performance showcase on GitHub for future advancements.

[Source Code] [Paper]

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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:
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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:
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🛞 6Img-to-3D driving scenarios 🛞

👮‍♀️ EPFL (+ Continental) unveils 6Img-to-3D, novel transformer-based encoder-renderer method to create 3D onbounded outdoor driving scenarios with only six pics

🥺 Review: https://shorturl.at/dZ018

🤨 Paper: arxiv.org/pdf/2404.12378.pdf

👉 Project: 6img-to-3d.github.io/

👉 Code: github.com/continental/6Img-to-3D

✅ https://t.me/deeplearning_ai

🔍 Introducing UniRef++: Advanced Object Segmentation in Spatial and Temporal Domains
🚀 Key Features:

Unified Model: UniRef++ seamlessly handles segmentation tasks:
✅Referring Image Segmentation (RIS)
✅Few-Shot Segmentation (FSS)
✅Referring Video Object Segmentation (RVOS)
✅Video Object Segmentation (VOS)

Core Component: UniFusion module
✅Integrates reference information efficiently
✅Utilizes flash attention for high efficiency

Compatibility: Acts as a plug-in for foundational models like SAM

🌐 UniRef++ is the official extended implementation from ICCV 2023's UniRef.

Stay tuned for more updates!

👉 Code: https://github.com/FoundationVision/UniRef
🤨 Paper: [Paper link]

✅ https://t.me/deeplearning_ai

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


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✅Model range: SCRFD_500M to SCRFD_34G
✅Accuracy up to 96.06%
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🔍 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.
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