4 citations · 13 across the 14 of their papers we have counts for
17 papers
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…
GATA2Floor: Graph attention for floor counting in street-view facades
Ngoc Tan Le, Tzoulio Chamiti, Eirini Papagiannopoulou +1
Automated analysis of building facades from street-level imagery has great potential for urban analytics, energy assessment, and emergency planning. However, it requires reasoning…
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…
Learned layered coding for Successive Refinement in the Wyner-Ziv Problem
Boris Joukovsky, Brent De Weerdt, Nikos Deligiannis
We propose a data-driven approach to explicitly learn the progressive encoding of a continuous source, which is successively decoded with increasing levels of quality and with the…
Holistic Representation Learning for Multitask Trajectory Anomaly Detection
Alexandros Stergiou, Brent De Weerdt, Nikos Deligiannis
Video anomaly detection deals with the recognition of abnormal events in videos. Apart from the visual signal, video anomaly detection has also been addressed with the use of skele…
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…
The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data
Luuk H. Boulogne, Julian Lorenz, Daniel Kienzle +35
Challenges drive the state-of-the-art of automated medical image analysis. The quantity of public training data that they provide can limit the performance of their solutions. Publ…
Leaping Into Memories: Space-Time Deep Feature Synthesis
Alexandros Stergiou, Nikos Deligiannis
The success of deep learning models has led to their adaptation and adoption by prominent video understanding methods. The majority of these approaches encode features in a joint s…
Entropy-Based Feature Extraction For Real-Time Semantic Segmentation
Lusine Abrahamyan, Nikos Deligiannis
This paper introduces an efficient patch-based computational module, coined Entropy-based Patch Encoder (EPE) module, for resource-constrained semantic segmentation. The EPE module…
Representation Learning with Information Theory for COVID-19 Detection
Abel Díaz Berenguer, Tanmoy Mukherjee, Matias Bossa +2
Successful data representation is a fundamental factor in machine learning based medical imaging analysis. Deep Learning (DL) has taken an essential role in robust representation l…
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…
Gradient Variance Loss for Structure-Enhanced Image Super-Resolution
Lusine Abrahamyan, Anh Minh Truong, Wilfried Philips +1
Recent success in the field of single image super-resolution (SISR) is achieved by optimizing deep convolutional neural networks (CNNs) in the image space with the L1 or L2 loss. H…
Bias Loss for Mobile Neural Networks
Lusine Abrahamyan, Valentin Ziatchin, Yiming Chen +1
Compact convolutional neural networks (CNNs) have witnessed exceptional improvements in performance in recent years. However, they still fail to provide the same predictive power a…
A Robust Deep Unfolded Network for Sparse Signal Recovery from Noisy Binary Measurements
Y. Yang, P. Xiao, B. Liao +1
We propose a novel deep neural network, coined DeepFPC-, for solving the 1-bit compressed sensing problem. The network is designed by unfolding the iterations of the fixed-…