most citedLess Precise Can Be More Reliable: A Systematic Evaluation of Quantization's Impact on VLMs Beyond Accuracy

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cs.CV2026

Leveraging Color Naming for Image Enhancement

David Serrano-Lozano, Luis Herranz, Michael S. Brown +1

Enhancing images to make them visually appealing is a persistent challenge in computer vision. Many deep-learning methods train models on paired datasets to replicate expert editin…

cs.CV2026

Temporal Modeling of Optically Variable Devices in Identity Documents

Glen Pouliquen, Joseph Chazalon, Guillaume Chiron +3

Robust remote verification of identity documents relies on analyzing faint, transparent security features like Optically Variable Devices (OVDs), or "holograms", within user-captur…

cs.CV2026★ 1 cited

Less Precise Can Be More Reliable: A Systematic Evaluation of Quantization's Impact on VLMs Beyond Accuracy

Aymen Bouguerra, Daniel Montoya, Alexandra Gomez-Villa +2

Vision-Language Models (VLMs) such as CLIP have revolutionized zero-shot classification and safety-critical tasks, including Out-of-Distribution (OOD) detection. However, their hig…