UniEval: Unified Holistic Evaluation for Unified Multimodal Understanding and Generation

Understand

The emergence of unified multimodal understanding and generation models is rapidly attracting attention because of their ability to enhance instruction-following capabilities while minimizing model redundancy.

• However, there is a lack of a unified evaluation framework for these models, which would enable an elegant, simplified, and overall evaluation.
• Current models conduct evaluations on multiple task-specific benchmarks, but there are significant limitations, such as the lack of overall results, errors from extra evaluation models, reliance on extensive labeled images, benchmarks that lack diversity, and metrics with limited capacity for instruction-following evaluation.
• To tackle these challenges, we introduce UniEval, the first evaluation framework designed for unified multimodal models without extra models, images, or annotations.