Segment-Everything-Everywhere- VS Segment-Everything-Everywhere-All-At-Once

Compare Segment-Everything-Everywhere- vs Segment-Everything-Everywhere-All-At-Once and see what are their differences.

Segment-Everything-Everywhere-

By UX-Decoder
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Segment-Everything-Everywhere-All-At-Once

[NeurIPS 2023] Official implementation of the paper "Segment Everything Everywhere All at Once" (by UX-Decoder)
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Segment-Everything-Everywhere- Segment-Everything-Everywhere-All-At-Once
2 6
- 4,064
- 2.8%
- 7.9
- about 1 month ago
Python
- Apache License 2.0
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Segment-Everything-Everywhere-

Posts with mentions or reviews of Segment-Everything-Everywhere-. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-10-28.
  • Is supervised learning dead for computer vision?
    9 projects | news.ycombinator.com | 28 Oct 2023
    Yes, you can. The model that I was talking about LLaVA only output text but other models such as SEEM (https://github.com/UX-Decoder/Segment-Everything-Everywhere-...) outputs a segmentation map. You could prompt the model "Where is the pickleball in the image?" and get a segmentation map that you could then use to compute its center. Please let me know if you would be interested to have SEEM available in Datasaurus

Segment-Everything-Everywhere-All-At-Once

Posts with mentions or reviews of Segment-Everything-Everywhere-All-At-Once. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-10-28.
  • Is supervised learning dead for computer vision?
    9 projects | news.ycombinator.com | 28 Oct 2023
    Yes, you can. The model that I was talking about LLaVA only output text but other models such as SEEM (https://github.com/UX-Decoder/Segment-Everything-Everywhere-...) outputs a segmentation map. You could prompt the model "Where is the pickleball in the image?" and get a segmentation map that you could then use to compute its center. Please let me know if you would be interested to have SEEM available in Datasaurus
  • The less i know the better
    2 projects | /r/StableDiffusion | 23 Jun 2023
    I think people are just seeing the rate of progress and rightfully think that this stuff will be possible at some point. For the rotoscoping for example, here's an example of progress being made on that.
  • A robot showing off his moves
    1 project | /r/oddlysatisfying | 2 May 2023
    Yeah, it's definitely possible especially with all the recent advances. With segment anything systems (like SAM) and segmentation on NeRF reconstructions already being a thing the feasibility of this is more a time investment thing. Naive "scene understanding" is already possible in a few AR headsets at real-time, but the new papers in the past few weeks have made this much more trivial and faster to implement.
  • Seem: Segment Everything Everywhere All at Once
    1 project | news.ycombinator.com | 14 Apr 2023
  • [R] SEEM: Segment Everything Everywhere All at Once
    2 projects | /r/MachineLearning | 13 Apr 2023
    Play with the demo on GitHub! https://github.com/UX-Decoder/Segment-Everything-Everywhere-All-At-Once

What are some alternatives?

When comparing Segment-Everything-Everywhere- and Segment-Everything-Everywhere-All-At-Once you can also consider the following projects:

LLaVA - [NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.

segment-anything - The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

LoRA - Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"

guidance - A guidance language for controlling large language models.

autodistill - Images to inference with no labeling (use foundation models to train supervised models).

datasaurus - Do computer vision with 1000x less data

Segment-Everything-Everywhere- vs LLaVA Segment-Everything-Everywhere-All-At-Once vs segment-anything Segment-Everything-Everywhere- vs LoRA Segment-Everything-Everywhere-All-At-Once vs LLaVA Segment-Everything-Everywhere- vs guidance Segment-Everything-Everywhere-All-At-Once vs guidance Segment-Everything-Everywhere- vs autodistill Segment-Everything-Everywhere-All-At-Once vs LoRA Segment-Everything-Everywhere- vs datasaurus Segment-Everything-Everywhere-All-At-Once vs autodistill