Datasets › ViP-Bench
ViP-Bench (Making Large Multimodal Models Understand Arbitrary Visual Prompts)
ViP-Bench is a comprehensive benchmark designed to assess the capability of multimodal models in understanding visual prompts across multiple dimensions. It aims to evaluate how well these models interpret various visual prompts, including recognition, OCR, knowledge, math, relationship reasoning, and language generation. ViP-Bench includes a diverse set of 303 images and questions, providing a thorough assessment of visual understanding capabilities at the region level. This benchmark sets a foundation for future research into multimodal models with arbitrary visual prompts.
Benchmarks archive 2025-07-28
All 1 leaderboard whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.
| First row (archive order) | Paper | Code | ||||
|---|---|---|---|---|---|---|
| Visual Question Answering | ViP-Bench | GPT-4V-turbo-detail:high (Visual Prompt) GPT-4 score (bbox) 60.7 | GPT-4 Technical Report | openai/evals +10 | 13 | Compare |
Papers archive 2025-07-28
9 shown of 9 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 10. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.
Dataset loaders archive 2025-07-28
No loader listed in the archive.
Tasks archive 2025-07-28
License archive 2025-07-28
Modalities archive 2025-07-28
Languages archive 2025-07-28
Variants archive 2025-07-28
- ViP-Bench
1 variant name, as the archive lists them.
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