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cs.CV2026

SigLino: Efficient Multi-Teacher Distillation for Agglomerative Vision Foundation Models

Sofian Chaybouti, Sanath Narayan, Yasser Dahou +6

Vision foundation models trained via multi-teacher distillation offer a promising path toward unified visual representations, yet the learning dynamics and data efficiency of such…

cs.CV2026

Falcon Perception

Aviraj Bevli, Sofian Chaybouti, Yasser Dahou +6

Perception-centric systems are typically implemented with a modular encoder-decoder pipeline: a vision backbone for feature extraction and a separate decoder (or late-fusion module…

cs.CV2026

MaskInversion: Localized Embeddings via Optimization of Explainability Maps

Walid Bousselham, Sofian Chaybouti, Christian Rupprecht +2

Vision-language foundation models such as CLIP have achieved tremendous results in global vision-language alignment, but still show some limitations in creating representations for…

cs.CV2025

VisRes Bench: On Evaluating the Visual Reasoning Capabilities of VLMs

Brigitta Malagurski Törtei, Yasser Dahou, Ngoc Dung Huynh +5

Vision-Language Models (VLMs) have achieved remarkable progress across tasks such as visual question answering and image captioning. Yet, the extent to which these models perform v…

cs.CV2025

REVEAL: Relation-based Video Representation Learning for Video-Question-Answering

Sofian Chaybouti, Walid Bousselham, Moritz Wolter +1

Video-Question-Answering (VideoQA) comprises the capturing of complex visual relation changes over time, remaining a challenge even for advanced Video Language Models (VLM), i.a.,…

cs.CV2025

LeGrad: An Explainability Method for Vision Transformers via Feature Formation Sensitivity

Walid Bousselham, Angie Boggust, Sofian Chaybouti +2

Vision Transformers (ViTs), with their ability to model long-range dependencies through self-attention mechanisms, have become a standard architecture in computer vision. However,…