4 citations · 6 across the 8 of their papers we have counts for
10 papers · 1 filter
On Test-Time Scaling for Vision-Language Models
Fawaz Sammani, Tzoulio Chamiti, Nikos Deligiannis
Test-time scaling is a paradigm where large models use additional compute at inference to achieve better performance, without changing model weights. While it has been widely studi…
When Negation Is a Geometry Problem in Vision-Language Models
Fawaz Sammani, Tzoulio Chamiti, Paul Gavrikov +1
Joint Vision-Language Embedding models such as CLIP typically fail at understanding negation in text queries, for example, failing to distinguish "no" in the query: "a plain blue s…
Temporal Concept Dynamics in Diffusion Models via Prompt-Conditioned Interventions
Ada Gorgun, Fawaz Sammani, Nikos Deligiannis +2
Diffusion models are usually evaluated by their final outputs, gradually denoising random noise into meaningful images. Yet, generation unfolds along a trajectory, and analyzing th…
CLIP-Free, Label Free, Unsupervised Concept Bottleneck Models
Fawaz Sammani, Jonas Fischer, Nikos Deligiannis
Concept Bottleneck Models (CBMs) map dense feature representations into human-interpretable concepts which are then combined linearly to make a prediction. However, modern CBMs rel…
Interpreting and Analysing CLIP's Zero-Shot Image Classification via Mutual Knowledge
Fawaz Sammani, Nikos Deligiannis
Contrastive Language-Image Pretraining (CLIP) performs zero-shot image classification by mapping images and textual class representation into a shared embedding space, then retriev…
Uni-NLX: Unifying Textual Explanations for Vision and Vision-Language Tasks
Fawaz Sammani, Nikos Deligiannis
Natural Language Explanations (NLE) aim at supplementing the prediction of a model with human-friendly natural text. Existing NLE approaches involve training separate models for ea…
Visualizing and Understanding Contrastive Learning
Fawaz Sammani, Boris Joukovsky, Nikos Deligiannis
Contrastive learning has revolutionized the field of computer vision, learning rich representations from unlabeled data, which generalize well to diverse vision tasks. Consequently…
NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language Tasks
Fawaz Sammani, Tanmoy Mukherjee, Nikos Deligiannis
Natural language explanation (NLE) models aim at explaining the decision-making process of a black box system via generating natural language sentences which are human-friendly, hi…
Show, Edit and Tell: A Framework for Editing Image Captions
Fawaz Sammani, Luke Melas-Kyriazi
Most image captioning frameworks generate captions directly from images, learning a mapping from visual features to natural language. However, editing existing captions can be easi…
Look and Modify: Modification Networks for Image Captioning
Fawaz Sammani, Mahmoud Elsayed
Attention-based neural encoder-decoder frameworks have been widely used for image captioning. Many of these frameworks deploy their full focus on generating the caption from scratc…