1.7k citations · 2.2k across the 42 of their papers we have counts for
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The Phantom Menace: Unmasking Privacy Leakages in Vision-Language Models
Simone Caldarella, Massimiliano Mancini, Elisa Ricci +1
Vision-Language Models (VLMs) combine visual and textual understanding, rendering them well-suited for diverse tasks like generating image captions and answering visual questions a…
Imperfect Vision Encoders: Efficient and Robust Tuning for Vision-Language Models
Aristeidis Panos, Rahaf Aljundi, Daniel Olmeda Reino +1
Vision language models (VLMs) demonstrate impressive capabilities in visual question answering and image captioning, acting as a crucial link between visual and language models. Ho…
Dual-Phase Continual Learning: Supervised Adaptation Meets Unsupervised Retention
Vaibhav Singh, Rahaf Aljundi, Eugene Belilovsky
Foundational Vision-Language Models (VLMs) excel across diverse tasks, but adapting them to new domains without forgetting prior knowledge remains a critical challenge. Continual L…
Annotation Free Semantic Segmentation with Vision Foundation Models
Soroush Seifi, Daniel Olmeda Reino, Fabien Despinoy +1
Semantic Segmentation is one of the most challenging vision tasks, usually requiring large amounts of training data with expensive pixel level annotations. With the success of foun…