Author

S. Sekeh

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#machine learning Jun 2026

Understanding Cross-Modal Contributions in Continual Vision-Language Models: A Theoretical Perspective

This paper presents a new theoretical perspective to understand the cross-modal (vision-language) contributions to consecutive environments, and provides deeper insights into continual VLMs, highlighting their contribution robustness to varying task orders and inter-task similarities, and their improved generalization...

S. Sekeh, M. Wisell · arXiv.org · 1 citation

Preprint Aug 2026

SABRE: A Multi-Agent Approach for Selecting Out-of-Distribution Detectors Under a Budget

This work introduces SABRE (Selective Agentic Budgeted Reliability Ensemble), which replaces this fixed choice with per-regime selection at inference in post-hoc out-of-distribution detection for vision-language models, and shows reliability must be established at deployment rather than assumed from a benchmark.

M. Wisell, S. Sekeh · 0 citations

Preprint Jul 2026

C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes

This work introduces C-GAP Caption-Guided Augmentation and Prompting), a detector-agnostic, annotation-free framework that operates in two phases, and establishes a composite caption baseline combining per-image scene descriptions with class-quantity context, which is shown to outperforms scene-description only or clas...

F. Fernández, A. Jahangiri, S. Sekeh · 0 citations