CAPTURe, a new benchmark for occluded pattern counting, shows that six vision-language models count far worse when objects are hidden, while humans make almost no errors.
OCC-MLLM:Empowering Multimodal Large Language Model For the Understanding of Occluded Objects
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abstract
There is a gap in the understanding of occluded objects in existing large-scale visual language multi-modal models. Current state-of-the-art multimodal models fail to provide satisfactory results in describing occluded objects for visual-language multimodal models through universal visual encoders. Another challenge is the limited number of datasets containing image-text pairs with a large number of occluded objects. Therefore, we introduce a novel multimodal model that applies a newly designed visual encoder to understand occluded objects in RGB images. We also introduce a large-scale visual-language pair dataset for training large-scale visual-language multimodal models and understanding occluded objects. We start our experiments comparing with the state-of-the-art models.
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CAPTURe: Evaluating Spatial Reasoning in Vision Language Models via Occluded Object Counting
CAPTURe, a new benchmark for occluded pattern counting, shows that six vision-language models count far worse when objects are hidden, while humans make almost no errors.