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Across three visual tasks, it is shown that pathway selection is task-dependent, and that data distribution and prompt design can also modulate which pathway is used to solve the image-based query.
Can a vision model learn what words mean from pixels alone? We show that the two largest DINOv3 vision transformers organize words rendered in images by word meaning, despite being pretrained using self-supervision on images only, without paired captions, class labels, or other language supervision. We derive word repr...
It is found that human-written samples result in higher agreement and allow greater control of dataset contents, while remaining distributionally similar to samples derived from vision-and-language samples and providing a reasonable portrayal of detection capabilities - suggesting that human data is a viable substitute...
This time, the SHROOM-Visions task aims to tackle hallucinations through a model-agnostic detection task focused on large vision-language models, building on the recently introduced SHEEP dataset.
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