1 citations · 1 across the 5 of their papers we have counts for
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Concept-Aware Batch Sampling Improves Language-Image Pretraining
Adhiraj Ghosh, Vishaal Udandarao, Thao Nguyen +7
What data should a vision-language model be trained on? To answer this question, many data curation efforts center on the quality of a dataset. However, most of these existing meth…
Improving Performance, Robustness, and Fairness of Radiographic AI Models with Finely-Controllable Synthetic Data
Stefania L. Moroianu, Christian Bluethgen, Pierre Chambon +8
Achieving robust performance and fairness across diverse patient populations remains a challenge in developing clinically deployable deep learning models for diagnostic imaging. Sy…
A Good CREPE needs more than just Sugar: Investigating Biases in Compositional Vision-Language Benchmarks
Vishaal Udandarao, Mehdi Cherti, Shyamgopal Karthik +3
We investigate 17 benchmarks (e.g. SugarCREPE, VALSE) commonly used for measuring compositional understanding capabilities of vision-language models (VLMs). We scrutinize design ch…
Scalable heliostat surface predictions from focal spots: Sim-to-Real transfer of inverse Deep Learning Raytracing
Jan Lewen, Max Pargmann, Jenia Jitsev +3
Concentrating Solar Power (CSP) plants are a key technology in the transition toward sustainable energy. A critical factor for their safe and efficient operation is the distributio…