XMem
[ECCV 2022] XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model (by hkchengrex)
Unicorn
[ECCV'22 Oral] Towards Grand Unification of Object Tracking (by MasterBin-IIAU)
| XMem | Unicorn | |
|---|---|---|
| 11 | 7 | |
| 1,594 | 942 | |
| - | - | |
| 6.3 | 0.0 | |
| about 2 months ago | over 1 year ago | |
| Python | Python | |
| MIT License | MIT License |
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?
Unicorn
Posts with mentions or reviews of Unicorn.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-08-06.
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need help with object detection and object tracking using yolov4
Also check out Unicorn - https://github.com/MasterBin-IIAU/Unicorn
- [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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Researchers from Bytedance and Dalian University Propose 🦄 ‘Unicorn’: a Unified Computer Vision Approach to Address Four Tracking Tasks Using a Single Model with the Same Model Parameters
Continue reading | Checkout the paper and github link
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[R] Unicorn: 🦄 : Towards Grand Unification of Object Tracking(Video Demo)
Brief Overview We present a unified method, termed Unicorn, that can simultaneously solve four tracking problems (SOT, MOT, VOS, MOTS) with a single network using the same model parameters. For the first time, we accomplished the great unification of the tracking network architecture and learning paradigm. Unicorn performs on-par or better than its task-specific counterparts in 8 tracking datasets, including LaSOT, TrackingNet, MOT17, BDD100K, DAVIS16-17, MOTS20, and BDD100K MOTS. Our work is accepted to ECCV 2022 as an oral presentation ! Paper: https://arxiv.org/abs/2207.07078 Code: https://github.com/MasterBin-IIAU/Unicorn
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[R] Unicorn: 🦄 : Towards Grand Unification of Object Tracking
Code for https://arxiv.org/abs/2207.07078 found: https://github.com/MasterBin-IIAU/Unicorn
What are some alternatives?
When comparing XMem and Unicorn 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
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.
flash-attention - Fast and memory-efficient exact attention
theseus - A library for differentiable nonlinear optimization
NAFNet - The state-of-the-art image restoration model without nonlinear activation functions.
latent-diffusion - High-Resolution Image Synthesis with Latent Diffusion Models
Cream - This is a collection of our NAS and Vision Transformer work. [Moved to: https://github.com/microsoft/AutoML]
NUWA - A unified 3D Transformer Pipeline for visual synthesis
EfficientZero - Open-source codebase for EfficientZero, from "Mastering Atari Games with Limited Data" at NeurIPS 2021.
hivemind - Decentralized deep learning in PyTorch. Built to train models on thousands of volunteers across the world.