7 papers
Personalization Toolkit: Training Free Personalization of Large Vision Language Models
Soroush Seifi, Vaggelis Dorovatas, Matteo Cassinelli +3
Personalization of Large Vision-Language Models (LVLMs) involves customizing models to recognize specific users or object instances and to generate contextually tailored responses.…
Ego: Embedding-Guided Personalization of Vision-Language Models
Soroush Seifi, Simon Gardier, Vaggelis Dorovatas +2
AI assistants that support humans in daily life are becoming increasingly feasible, driven by the rapid advancements in multimodal language models. A key challenge lies in overcomi…
ACDC: The Adverse Conditions Dataset with Correspondences for Robust Semantic Driving Scene Perception
Christos Sakaridis, Haoran Wang, Ke Li +6
Level-5 driving automation requires a robust visual perception system that can parse input images under any condition. However, existing driving datasets for dense semantic percept…
Efficient Few-Shot Continual Learning in Vision-Language Models
Aristeidis Panos, Rahaf Aljundi, Daniel Olmeda Reino +1
Vision-language models (VLMs) excel in tasks such as visual question answering and image captioning. However, VLMs are often limited by their use of pretrained image encoders, like…
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…
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…