How to make LLM output object-centered visual annotations without decoding the LLM's output, so that it does not generate text but only produces segmented images.
Open
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 1.4k
- Forks
- 55
- PR merge metrics
- No merged PRs in 30d
Description
How to make LLM output object-centered visual annotations without decoding the LLM's output, so that it does not generate text but only produces segmented images.
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
The issue names no files, tests, or entry points. Start by reviewing the existing OMG-Seg and OMG-LLaVA inference flow, then clarify whether the request is for implementation guidance or a repository change. Done would require a defined, reproducible way to produce object-centered segmented images without text output.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- computer-vision, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100