XMem
[ECCV 2022] XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model (by hkchengrex)
EfficientZero
Open-source codebase for EfficientZero, from "Mastering Atari Games with Limited Data" at NeurIPS 2021. (by YeWR)
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| XMem | EfficientZero | |
|---|---|---|
| 11 | 9 | |
| 1,588 | 825 | |
| - | - | |
| 6.3 | 0.0 | |
| about 1 month ago | 4 months ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 only |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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
Posts with mentions or reviews of XMem.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-06-11.
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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?
EfficientZero
Posts with mentions or reviews of EfficientZero.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-04-19.
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[D] GPT-3T: Can we train language models to think further ahead?
Here's an algorithm that is more sample efficient : https://github.com/YeWR/EfficientZero
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MuZero learns to play Teamfight Tactics
Use multiprocessing to have more GPU workers could help. My code based on EfficientZero https://github.com/YeWR/EfficientZero is utilizing CPUs and GPUs to 90%. It uses Ray for multiprocessing and splits Reanalyze into CPU and GPU workers to maximize resource utilization. By the way, it's not converging to optimal policy well: it gets stuck at 50% optimal episode return at with a small amount of training. Have you had this issue before?
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[R] Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning - Epochai Pablo Villalobos et al - Trend of ever-growing ML models might slow down if data efficiency is not drastically improved!
Found relevant code at https://github.com/YeWR/EfficientZero + all code implementations here
- Anyone found any working replication repo for MuZero?
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[D] Most important AI Paper´s this year so far in my opinion + Proto AGI speculation at the end
Mastering Atari Games with Limited Data – EfficientZero ( Human sample -efficiency! ) Paper: https://arxiv.org/abs/2111.00210 Lesswrong article about the paper: https://www.lesswrong.com/posts/mRwJce3npmzbKfxws/efficientzero-how-it-works Github: https://github.com/YeWR/EfficientZero
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Waymo To Use Chinese Geely Robotaxi Body. This Should Send Shivers Into Western OEMs
Have you seen https://github.com/YeWR/EfficientZero EfficientZero yet? This agent is able to solve problems with unknown rules, where the agent starts only with information about the shape of the inputs and reward feedback. With superhuman ability - it needs less training data than humans do - and SoTA trumping results on the problems it has been tried on. (various atari/Go/chess/etc)
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Why does EfficientZero use SimSiam for temporal consistency instead of MAE / MSE?
Open-source codebase for EfficientZero - am I missing something or the repo is empty?
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[D] Paper Explained - EfficientZero: Mastering Atari Games with Limited Data (Full Video Analysis)
Code: https://github.com/YeWR/EfficientZero
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"EfficientZero: Mastering Atari Games with Limited Data", Ye et al 2021 (beating humans on ALE-100k/2h by adding self-supervised learning to MuZero-Reanalyze)
Code for https://arxiv.org/abs/2111.00210 found: https://github.com/YeWR/EfficientZero
What are some alternatives?
When comparing XMem and EfficientZero you can also consider the following projects:
yolov7 - Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
flash-attention - Fast and memory-efficient exact attention
flash-attention-jax - Implementation of Flash Attention in Jax
NAFNet - The state-of-the-art image restoration model without nonlinear activation functions.
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.
RHO-Loss
Cream - This is a collection of our NAS and Vision Transformer work. [Moved to: https://github.com/microsoft/AutoML]
CodeRL - This is the official code for the paper CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning (NeurIPS22).
multiface - Hosts the Multiface dataset, which is a multi-view dataset of multiple identities performing a sequence of facial expressions.
msn - Masked Siamese Networks for Label-Efficient Learning (https://arxiv.org/abs/2204.07141)