π₯The power of Transformersπ₯
π100+ official implementations, papers, github repo and colab of:
01 GPT-Neo 2021
02 Transformer 2017
03 BERT 2018
04 GPT 2018
05 Univ.Transformer 2018
06 T-D 2018
07 GPT-2 2019
08 T5 2019
09 BART 2019
10 XLNet 2019
11...
πThe full list: https://github.com/ashishpatel26/Treasure-of-Transformers
π100+ official implementations, papers, github repo and colab of:
01 GPT-Neo 2021
02 Transformer 2017
03 BERT 2018
04 GPT 2018
05 Univ.Transformer 2018
06 T-D 2018
07 GPT-2 2019
08 T5 2019
09 BART 2019
10 XLNet 2019
11...
πThe full list: https://github.com/ashishpatel26/Treasure-of-Transformers
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βοΈTransformers in Medical βοΈ
π100+ papers, implementations and code of Transformers in medical imaging.
ππ’π π‘π₯π’π π‘ππ¬:
β Medical Image Segmentation
β Medical Image Classification
β Medical Image Reconstruction
β Medical Image Registration
β Medical Image Synthesis
β Medical Image Detection
β Clinical Report Generation
β Survey and more..
The full list: https://bit.ly/3ILzswl
π100+ papers, implementations and code of Transformers in medical imaging.
ππ’π π‘π₯π’π π‘ππ¬:
β Medical Image Segmentation
β Medical Image Classification
β Medical Image Reconstruction
β Medical Image Registration
β Medical Image Synthesis
β Medical Image Detection
β Clinical Report Generation
β Survey and more..
The full list: https://bit.ly/3ILzswl
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πͺ#3D with Transformersπͺ
πShapeFormer, transformer network for incomplete input
β VQDIF representation for 3D
β Transformer-based model
β Partial input -> completed shape
β ConvONet/Taming-transf/DCTransf.
β SOTA for #3D shape completion
More: https://bit.ly/3s0D2f1
πShapeFormer, transformer network for incomplete input
β VQDIF representation for 3D
β Transformer-based model
β Partial input -> completed shape
β ConvONet/Taming-transf/DCTransf.
β SOTA for #3D shape completion
More: https://bit.ly/3s0D2f1
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π±YOLO5 real-time logo detectorπ±
ππ’π π‘π₯π’π π‘ππ¬:
β Based on YOLOv5 family
β Google Colab + Azure
β 90k pics, training: 2 weeks
β Pretrained models/code
β GNU License v3.0
More: https://bit.ly/3r8Qoa7
ππ’π π‘π₯π’π π‘ππ¬:
β Based on YOLOv5 family
β Google Colab + Azure
β 90k pics, training: 2 weeks
β Pretrained models/code
β GNU License v3.0
More: https://bit.ly/3r8Qoa7
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π¦RelTR: #AI scene-graphsπ¦
πOne-stage method for object relationship via visual appearance only.
ππ’π π‘π₯π’π π‘ππ¬:
β RelTR ,end-to-end framework
β Classifying dense relationships
β Scene graphs on appearance only
β No combining entities & labeling
β Superior performance, faster
More: https://bit.ly/3r8k86Y
πOne-stage method for object relationship via visual appearance only.
ππ’π π‘π₯π’π π‘ππ¬:
β RelTR ,end-to-end framework
β Classifying dense relationships
β Scene graphs on appearance only
β No combining entities & labeling
β Superior performance, faster
More: https://bit.ly/3r8k86Y
π4π₯2π€―1π€©1
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π₯ΆSOTA in crowd analysis is INSANEπ₯Ά
πTencent unveils P2PNet to predict heads in images
ππ’π π‘π₯π’π π‘ππ¬:
β Pure point counting/detecting
β Normalized Average Precision
β VGG16-like architecture
β Simultaneous point/confidence
β License: only academic
More: https://bit.ly/33UjoK0
πTencent unveils P2PNet to predict heads in images
ππ’π π‘π₯π’π π‘ππ¬:
β Pure point counting/detecting
β Normalized Average Precision
β VGG16-like architecture
β Simultaneous point/confidence
β License: only academic
More: https://bit.ly/33UjoK0
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βοΈOLSO: Transformers OptimizationβοΈ
ππ’π π‘π₯π’π π‘ππ¬:
β Automagical with Hugging Face
β GPU-based optimizations
β Easily installation with pip
β Apache License 2.0
More: https://bit.ly/3r8wY58
ππ’π π‘π₯π’π π‘ππ¬:
β Automagical with Hugging Face
β GPU-based optimizations
β Easily installation with pip
β Apache License 2.0
More: https://bit.ly/3r8wY58
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π¦ΎSOTA in robotic manipulationπ¦Ύ
ππ’π π‘π₯π’π π‘ππ¬:
β VCD: Visible Connectivity Dynamics
β VCG: Visible Connectivity Graph
β Dynamics model over this VCG
β Handling material, geometry, color
β SOTA vs. model-based/model-free RL
β Source code and models available
More: https://bit.ly/3HhusiH
ππ’π π‘π₯π’π π‘ππ¬:
β VCD: Visible Connectivity Dynamics
β VCG: Visible Connectivity Graph
β Dynamics model over this VCG
β Handling material, geometry, color
β SOTA vs. model-based/model-free RL
β Source code and models available
More: https://bit.ly/3HhusiH
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πVRT: new SOTA in super resolutionπ
ππ’π π‘π₯π’π π‘ππ¬:
β Image restoration via Swin
β Residual Swin Transf. Blocks
β SOTA in Artifact Reduction
β SOTA in Super-resolution
β SOTA in Denoising
β Parameters -67%!
β Non commercial π₯²
More: https://bit.ly/3rfAta1
ππ’π π‘π₯π’π π‘ππ¬:
β Image restoration via Swin
β Residual Swin Transf. Blocks
β SOTA in Artifact Reduction
β SOTA in Super-resolution
β SOTA in Denoising
β Parameters -67%!
β Non commercial π₯²
More: https://bit.ly/3rfAta1
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π¦The new #MediaPipe is INSANE π¦
πGoogle just launched two new highly optimized body segmentation models
ππ’π π‘π₯π’π π‘ππ¬:
β Full body 3D pose
β Designed for yoga, fitness & dance
β Measurements for virtual tailor
β Selfie Segmentation on call
More: https://bit.ly/3s6sjjx
πGoogle just launched two new highly optimized body segmentation models
ππ’π π‘π₯π’π π‘ππ¬:
β Full body 3D pose
β Designed for yoga, fitness & dance
β Measurements for virtual tailor
β Selfie Segmentation on call
More: https://bit.ly/3s6sjjx
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π₯Έ Clothed avatars for #metaverse π₯Έ
πTelepresence, AR/VR, anthropometry, and virtual try-on.
ππ’π π‘π₯π’π π‘ππ¬:
β Differential loss of explicit mesh
β Details via neural rendering
β Explicit mesh updating
β Consistency loss for quality++
β Hi-Fi surfaces by S.S. optimization
More: https://bit.ly/3ohAN6d
πTelepresence, AR/VR, anthropometry, and virtual try-on.
ππ’π π‘π₯π’π π‘ππ¬:
β Differential loss of explicit mesh
β Details via neural rendering
β Explicit mesh updating
β Consistency loss for quality++
β Hi-Fi surfaces by S.S. optimization
More: https://bit.ly/3ohAN6d
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π¦JoJoGAN: One Shot Face Stylizationπ¦
πUIUC researchers unveil a novel method for one-shot image stylization.
ππ’π π‘π₯π’π π‘ππ¬:
β Stylization from single input
β Finetuning StyleGAN for stylization
β No supervision, good generalization
β MIT License (commercial allowed)
More: https://bit.ly/3ASVzyb
πUIUC researchers unveil a novel method for one-shot image stylization.
ππ’π π‘π₯π’π π‘ππ¬:
β Stylization from single input
β Finetuning StyleGAN for stylization
β No supervision, good generalization
β MIT License (commercial allowed)
More: https://bit.ly/3ASVzyb
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π§¦SOTA in OOD detection for safer #AIπ§¦
πOut-of-distribution (OOD) detection produces wrong/overconfident predictions.
ππ’π π‘π₯π’π π‘ππ¬:
β Novel framework for OOD
β Synthesizing virtual outliers
β Novel unknown-aware training
β Code and model available
More: https://bit.ly/3JnFIL9
πOut-of-distribution (OOD) detection produces wrong/overconfident predictions.
ππ’π π‘π₯π’π π‘ππ¬:
β Novel framework for OOD
β Synthesizing virtual outliers
β Novel unknown-aware training
β Code and model available
More: https://bit.ly/3JnFIL9
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π
StyleGAN-XL neural synthesisπ
πFrom TΓΌbingen, StyleGAN-XL: new SOTA for large diverse dataset.
ππ’π π‘π₯π’π π‘ππ¬:
β First 1024p-gen for large data
β Growing strategy on StyleGAN3
β Beyond the narrow domains
β Pivotal Tuning Inversion (TPI)
β SOTA vs. GAN & diffusion models
More: https://bit.ly/3HK9MQk
πFrom TΓΌbingen, StyleGAN-XL: new SOTA for large diverse dataset.
ππ’π π‘π₯π’π π‘ππ¬:
β First 1024p-gen for large data
β Growing strategy on StyleGAN3
β Beyond the narrow domains
β Pivotal Tuning Inversion (TPI)
β SOTA vs. GAN & diffusion models
More: https://bit.ly/3HK9MQk
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πThis keypoint is pure GLUEπ
πKeypoints play a central role in computer vision.
ππ’π π‘π₯π’π π‘ππ¬:
β Novel Object-centric keypoint
β Novel sim2real training method
β Intra-salience / inter-distinctness
β Enforcing semantic consistency
β Close to fully-supervised method!
More: https://bit.ly/3rth1qh
πKeypoints play a central role in computer vision.
ππ’π π‘π₯π’π π‘ππ¬:
β Novel Object-centric keypoint
β Novel sim2real training method
β Intra-salience / inter-distinctness
β Enforcing semantic consistency
β Close to fully-supervised method!
More: https://bit.ly/3rth1qh
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π‘ LEDNet: seeing in the dark π‘
πResearchers from NTU unveil LEDNet to see in the dark
ππ’π π‘π₯π’π π‘ππ¬:
β Novel data synthesis for low-light
β Low-light/deblurring dataset
β 12k low-blur/normal-sharp pairs
β LEDNet: lowlight + deblurring
More: https://bit.ly/3HIyYqM
πResearchers from NTU unveil LEDNet to see in the dark
ππ’π π‘π₯π’π π‘ππ¬:
β Novel data synthesis for low-light
β Low-light/deblurring dataset
β 12k low-blur/normal-sharp pairs
β LEDNet: lowlight + deblurring
More: https://bit.ly/3HIyYqM
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π©βπ¦°Back in the 50's with GANπ©βπ¦°
ππ’π π‘π₯π’π π‘ππ¬:
β A few thousand vintage faces
β Models available for download
β Stylegan2-ffhqu-1024x1024
β NO Commercial allowed
More: https://bit.ly/3LlOyKX
ππ’π π‘π₯π’π π‘ππ¬:
β A few thousand vintage faces
β Models available for download
β Stylegan2-ffhqu-1024x1024
β NO Commercial allowed
More: https://bit.ly/3LlOyKX
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π¦ VNCA: bio-inspired generative model π¦
πA novel generative model loosely inspired by the biological processes of cellular growth and differentiation
ππ’π π‘π₯π’π π‘ππ¬:
β Variational Neural Cellular Automata
β Probabilistic generative model
β Learn from common vector format
β Learn purely s.o. generative process
β Far away from SOTA, but interesting
More: https://bit.ly/3oGb2wG
πA novel generative model loosely inspired by the biological processes of cellular growth and differentiation
ππ’π π‘π₯π’π π‘ππ¬:
β Variational Neural Cellular Automata
β Probabilistic generative model
β Learn from common vector format
β Learn purely s.o. generative process
β Far away from SOTA, but interesting
More: https://bit.ly/3oGb2wG
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πBlock-NeRF: Neural View Synthesisπ
πLarge-scale scene reconstruction by multiple compact NeRFs that each fit into memory.
ππ’π π‘π₯π’π π‘ππ¬:
β Berkeley + Google + Waymo = π€―
β Scaling NeRF to city-scale scenes
β Trick: multiple simple NeRFs
β Time decoupled, arbitrarily large scene
β Data over months & different conditions
More: https://bit.ly/3GGVHBV
πLarge-scale scene reconstruction by multiple compact NeRFs that each fit into memory.
ππ’π π‘π₯π’π π‘ππ¬:
β Berkeley + Google + Waymo = π€―
β Scaling NeRF to city-scale scenes
β Trick: multiple simple NeRFs
β Time decoupled, arbitrarily large scene
β Data over months & different conditions
More: https://bit.ly/3GGVHBV
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π₯¬HW-Accelerated Neuro-Evolutionπ₯¬
πScalable, general purpose, hardware accelerated neuro-evolution toolkit by Google
ππ’π π‘π₯π’π π‘ππ¬:
β Parallel on multiple TPU/GPUs
β Neuro-evo algorithms with NNs
β WaterWorld, Abstract paint, more
β From Google, not an official product
β Code under Apache License 2.0
More: https://bit.ly/3szEi9w
πScalable, general purpose, hardware accelerated neuro-evolution toolkit by Google
ππ’π π‘π₯π’π π‘ππ¬:
β Parallel on multiple TPU/GPUs
β Neuro-evo algorithms with NNs
β WaterWorld, Abstract paint, more
β From Google, not an official product
β Code under Apache License 2.0
More: https://bit.ly/3szEi9w
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