UniEval: Unified Holistic Evaluation for Unified Multimodal Understanding and Generation

TL;DR

UniEval introduces a comprehensive, unified evaluation framework for multimodal models, enabling simplified, diverse, and human-aligned assessment without extra data or models, advancing the evaluation of multimodal understanding and generation.

Contribution

It presents the first unified evaluation framework, UniEval, including UniBench and UniScore, for assessing multimodal models holistically without additional models or annotations.

Findings

UniBench is more challenging than existing benchmarks.

UniScore closely aligns with human evaluations.

Extensive evaluation reveals new insights into state-of-the-art models.

Abstract

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. This facilitates a simplified and unified evaluation process. The UniEval framework contains a holistic benchmark, UniBench (supports both unified and visual generation models), along with the corresponding UniScore metric. UniBench includes 81 fine-grained tags contributing to high diversity. Experimental results indicate that UniBench is more challenging than existing benchmarks, and UniScore aligns closely with human evaluations, surpassing current metrics. Moreover, we extensively evaluated SoTA unified and visual generation models, uncovering new insights into Univeral's unique values.