open-compass / open-compass/VLMEvalKit
Proposal to Add “Document Understanding” Section to the Open VLM Leaderboard
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- Dominant language
- Python
- Stars
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Description
Propose the addition of a “Document Understanding” section to the Open VLM Leaderboard. This new section would focus on evaluating models’ capabilities in comprehending and processing document-based information, which is a critical aspect of vision-language integration for enterprise use cases.
Proposed Benchmark Datasets to be included in Document Understanding:
- DocVQA
- ChartVQA
- TextVQA
- InfoVQA
- LiveXivVQA
- LiveXivTQA
Contributor guide
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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
Start by reviewing how the Open VLM Leaderboard currently organizes evaluation sections and registers benchmark datasets. Define the scope for a new Document Understanding section covering DocVQA, ChartVQA, TextVQA, InfoVQA, LiveXivVQA, and LiveXivTQA. Done means the section and its dataset coverage are implemented, but this issue does not identify the relevant files or tests.
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
- 25/100