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Articles

CAVE: Detecting and Explaining Commonsense Anomalies in Visual Environments

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By Syrielle Montariol, Angelika Romanou, Beatriz Borges, Irina Rish
| arxiv.org
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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DRIVINGVQA: Analyzing Visual Chain-of-Thought Reasoning of Vision Language Models in Real-World Scenarios with Driving Theory Tests

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By Syrielle Montariol, Antoine Bosselut, Alexandre Alahi, Simon Roburin
| arxiv.org
[Submitted on 8 Jan 2025] Title:DRIVINGVQA: Analyzing Visual Chain-of-Thought Reasoning of Vision Language Models in Real-World Scenarios with Driving Theory Tests View a PDF of the paper titled DRIVINGVQA: Analyzing Visual Chain-of-Thought Reasoning of Vision Language Models in Real-World Scenarios with Driving Theory Tests, by Charles Corbi\`ere and 3 other authors View PDF HTML (experimental) Abstract:Large vision-language models (LVLMs) augment language models with visual understanding,...
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[2404.10876] Course Recommender Systems Need to Consider the Job Market

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By Anna Chuwen Dai, Syrielle Montariol, Antoine Bosselut, Tanja Käser
| arxiv.org
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