The paper introduces TextVQA-C and GQA-C, showing that LLaVA 1.5 loses text-question accuracy most under blur and snow and object-question accuracy most under frost and impulse noise, though only one model is tested.
Point-bert: Pre-training 3d point cloud transformers with masked point modeling,
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Analysing the Robustness of Vision-Language-Models to Common Corruptions
The paper introduces TextVQA-C and GQA-C, showing that LLaVA 1.5 loses text-question accuracy most under blur and snow and object-question accuracy most under frost and impulse noise, though only one model is tested.