XMem
NAFNet
| XMem | NAFNet | |
|---|---|---|
| 11 | 5 | |
| 1,735 | 2,178 | |
| - | 1.5% | |
| 6.3 | 0.0 | |
| 7 months ago | 3 months ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
XMem
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[D] Which open source models can replicate wonder dynamics's drag'n'drop cg characters?
Use Segmentation Model (SAM) combined with Inpainting model (E2FGVI) and Xmem to cut out the live action subject.
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Track-Anything: a flexible and interactive tool for video object tracking and segmentation, based on Segment Anything and XMem.
Nvm just found the occlusion video on https://github.com/hkchengrex/XMem holy shit
- XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model
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[D] Most important AI Paper´s this year so far in my opinion + Proto AGI speculation at the end
XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model ( Added because of the Atkinson-Shiffrin Memory Model ) Paper: https://arxiv.org/abs/2207.07115 Github: https://github.com/hkchengrex/XMem
- [D] Most Popular AI Research July 2022 pt. 2 - Ranked Based On GitHub Stars
- Most Popular AI Research July 2022 pt. 2 - Ranked Based On GitHub Stars
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I trained a neural net to watch Super Smash Bros
Yeah MiVOS would speed up your tagging a lot. I also was curious if you saw XMem which just came out. I found that worked really well too.
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University of Illinois Researchers Develop XMem; A Long-Term Video Object Segmentation Architecture Inspired By Atkinson-Shiffrin Memory Model
Continue reading | Check out the paper and github link.
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[R] Unicorn: 🦄 : Towards Grand Unification of Object Tracking(Video Demo)
Have you check XMem?
NAFNet
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Workflow to denoise low quality images?
I've tried using NAFNet but this simply does not work on most images I've tried. I've tried using VEAencode then using low denoise parameter on a KSampler while describing the image as much as possible and did not get good results as well.
- Best AI models for image resolution upscaling?
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Denoising image gives corrupted result
I am currently trying out different denoising techniques, and processed my dataset using NAFNet's Denoising model. However, I am getting a corrupted image as a result when processing certain ones. Down below is an example. Does anyone have an explanation for this?
- NAFNet: Nonlinear Activation Free Network for Image Restoration
What are some alternatives?
yolov7 - Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
MPRNet - [CVPR 2021] Multi-Stage Progressive Image Restoration. SOTA results for Image deblurring, deraining, and denoising.
flash-attention - Fast and memory-efficient exact attention
Real-ESRGAN-colab - A Real-ESRGAN model trained on a custom dataset
hivemind - Decentralized deep learning in PyTorch. Built to train models on thousands of volunteers across the world.
Restormer - [CVPR 2022--Oral] Restormer: Efficient Transformer for High-Resolution Image Restoration. SOTA for motion deblurring, image deraining, denoising (Gaussian/real data), and defocus deblurring.
NUWA - A unified 3D Transformer Pipeline for visual synthesis
DeblurGANv2 - [ICCV 2019] "DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better" by Orest Kupyn, Tetiana Martyniuk, Junru Wu, Zhangyang Wang
deeplab2 - DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a unified and state-of-the-art TensorFlow codebase for dense pixel labeling tasks.
FBCNN - Official Code for ICCV 2021 paper "Towards Flexible Blind JPEG Artifacts Removal (FBCNN)"
EfficientZero - Open-source codebase for EfficientZero, from "Mastering Atari Games with Limited Data" at NeurIPS 2021.
deep-tempest - Restoration for TEMPEST images using deep-learning