Challenging the Foundations: Mining Hard Test Samples through Diffusion Generation
Keywords: Large foundation models, Diffusion models, Vulnerability
Abstract: Large foundation models have achieved tremendous success with impressive performance in multiple applications. However, their performance is often benchmarked on natural images, where novel combinations of specific objects and nuisances can be missing and not tested. In this work, we develop a framework to efficiently probe foundation models for their vulnerabilities with diffusion generation, termed DiffusionExplorer. We show that our framework can efficiently construct a test set with novel combinations of object and nuisance factors to expose the failures of foundation models. Experimental results show that our mined test samples are challenging to foundation models, such as MiniGPT-4 and LLaVa, significantly reducing their accuracy by 29.56\% and 39.96\%, respectively. Our work suggests that generative models can be viewed as an effective data source in finding the vulnerability of large vision foundation models.
Primary Area: datasets and benchmarks
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Submission Number: 25
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