The MMVP-VLM (Multimodal Visual Patterns - Visual Language Models) Benchmark is specifically designed to systematically evaluate the performance of recent CLIP-based models in understanding and processing visual patterns. Let's delve into the details:

• Purpose: The MMVP-VLM Benchmark aims to assess how well CLIP models can match image-text combinations that represent distinct visual patterns. It distills a subset of questions from the original MMVP benchmark into simpler language descriptions, categorizing them into different visual patterns. Each visual pattern is represented by 15 text-image pairs.

• Dataset Composition:

  • Text-Image Pairs: The benchmark includes a balanced number of questions for each visual pattern, with each pattern represented by 15 pairs. These pairs are a subset of the MMVP benchmark, supplemented with additional questions for balance.
  • Visual Patterns: The questions cover various visual patterns, allowing evaluation of CLIP models' ability to understand and process these patterns.

• Insights and Limitations: By assessing whether CLIP models can accurately match the provided image-text combinations, the MMVP-VLM Benchmark provides insights into the capabilities and limitations of these models.