6 citations · 6 across the 8 of their papers we have counts for
10 papers
Dissecting Training-Free Uncertainty Estimation in Multimodal Large Language Models
Soroush Seifi, Vaggelis Dorovatas, Lin Li +2
Multimodal Large Language Models (MLLMs) are increasingly deployed in safety-critical settings, yet estimating when their answers can be trusted remains underexplored. We systemati…
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…
Recurrent Attention-based Token Selection for Efficient Streaming Video-LLMs
Vaggelis Dorovatas, Soroush Seifi, Gunshi Gupta +1
Video Large Language Models (Video-LLMs) excel at understanding videos in-context, provided they have full access to the video when answering queries. However, these models face ch…
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.…
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…
OOD Aware Supervised Contrastive Learning
Soroush Seifi, Daniel Olmeda Reino, Nikolay Chumerin +1
Out-of-Distribution (OOD) detection is a crucial problem for the safe deployment of machine learning models identifying samples that fall outside of the training distribution, i.e.…