task_categories:
- image-text-to-text
MVI-Bench
MVI-Bench is a comprehensive benchmark specifically designed to evaluate the robustness of Large Vision-Language Models (LVLMs) against misleading visual inputs.
- Paper: MVI-Bench: A Comprehensive Benchmark for Evaluating Robustness to Misleading Visual Inputs in LVLMs
- Repository: https://github.com/chenyil6/MVI-Bench
Introduction
Grounded in fundamental visual primitives, the design of MVI-Bench centers on three hierarchical levels of misleading visual inputs:
- Visual Concept
- Visual Attribute
- Visual Relationship
Using this taxonomy, the benchmark compiles 1,248 expertly annotated VQA instances across six representative categories. It also introduces MVI-Sensitivity, a novel metric that characterizes LVLM robustness at a granular level. Empirical results across 18 state-of-the-art LVLMs uncover pronounced vulnerabilities to misleading visual inputs.
Dataset Structure
The benchmark evaluates how misleading visual information affects model understanding and decision-making in visual question-answering tasks. It covers 1,248 instances designed to test models against various visual misinformation scenarios across different levels of complexity.