14 citations · 32 across the 21 of their papers we have counts for
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cs.CV2024★ 1 cited
Two Effects, One Trigger: On the Modality Gap, Object Bias, and Information Imbalance in Contrastive Vision-Language Models
Simon Schrodi, David T. Hoffmann, Max Argus +2
Contrastive vision-language models (VLMs), like CLIP, have gained popularity for their versatile applicability to various downstream tasks. Despite their successes in some tasks, l…
cs.CV2024★ 2 cited
Open-ended VQA benchmarking of Vision-Language models by exploiting Classification datasets and their semantic hierarchy
Simon Ging, María A. Bravo, Thomas Brox
The evaluation of text-generative vision-language models is a challenging yet crucial endeavor. By addressing the limitations of existing Visual Question Answering (VQA) benchmarks…