Dynamic Multimodal Evaluation with Flexible Complexity by Vision-Language Bootstrapping

• URL: http://arxiv.org/abs/2410.08695v2
• Date: Tue, 5 Nov 2024 03:56:21 GMT
• Title: Dynamic Multimodal Evaluation with Flexible Complexity by Vision-Language Bootstrapping
• Authors: Yue Yang, Shuibai Zhang, Wenqi Shao, Kaipeng Zhang, Yi Bin, Yu Wang, Ping Luo,
• Abstract summary: Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities across multimodal tasks. We introduce a dynamic multimodal evaluation protocol called Vision-Language Bootstrapping (VLB) VLB provides a robust and comprehensive assessment for LVLMs with reduced data contamination and flexible complexity.
• Score: 45.584695790489484
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities across multimodal tasks such as visual perception and reasoning, leading to good performance on various multimodal evaluation benchmarks. However, these benchmarks keep a static nature and overlap with the pre-training data, resulting in fixed complexity constraints and data contamination issues. This raises the concern regarding the validity of the evaluation. To address these two challenges, we introduce a dynamic multimodal evaluation protocol called Vision-Language Bootstrapping (VLB). VLB provides a robust and comprehensive assessment for LVLMs with reduced data contamination and flexible complexity. To this end, VLB dynamically generates new visual question-answering samples through a multimodal bootstrapping module that modifies both images and language, while ensuring that newly generated samples remain consistent with the original ones by a judge module. By composing various bootstrapping strategies, VLB offers dynamic variants of existing benchmarks with diverse complexities, enabling the evaluation to co-evolve with the ever-evolving capabilities of LVLMs. Extensive experimental results across multiple benchmarks, including SEEDBench, MMBench, and MME, show that VLB significantly reduces data contamination and exposes performance limitations of LVLMs.

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