10 papers
SABRE: A Multi-Agent Approach for Selecting Out-of-Distribution Detectors Under a Budget
Mary Wisell, Salimeh Sekeh
Post-hoc out-of-distribution (OOD) detection for vision-language models assumes that a detector chosen on a benchmark stays reliable once deployed. We show this fails across domain…
C-GAP: Class-Aware and Online Prompting Improves Vision-Language Models on Imbalanced Classes
Francis Fernandez, Arash Jahangiri, Salimeh Sekeh
Safety-critical perception systems must reliably detect rare object classes within small label spaces, a setting that long-tailed detection methods, designed for hundreds of classe…
Cross-Contextual Vision-Language Adaptation with LoRA for Personalized Severe Adverse Event Detection in Clinical Wound Monitoring
Aditi Naiknaware, Jian Sun, Aminreza Khandan +4
Wound monitoring is a critical yet underserved clinical challenge, where timely identification of severe adverse events (SAEs) such as infection, tissue deterioration, and delayed…
T-QPM: Enabling Temporal Out-Of-Distribution Detection and Domain Generalization for Vision-Language Models in Open-World
Aditi Naiknaware, Salimeh Sekeh
Out-of-distribution (OOD) detection remains a critical challenge in open-world learning, where models must adapt to evolving data distributions. While recent vision-language models…
Theoretical Grounding of Out-Of-Distribution Detection With Reinforcement Learning Optimizer
Salimeh Sekeh, Xin Zhang
Out-of-distribution (OOD) detection in dynamic open-world environments requires a model to continually adapt to evolving data distributions while generalizing to covariate-shifted…
GRASP: Gradient-Aligned Sequential Parameter Transfer for Memory-Efficient Multi-Source Learning
Mary Isabelle Wisell, Nicholas Jacobs, Aayush Manandhar +1
Multi-source transfer learning faces a fundamental scalability bottleneck: existing approaches require either loading all K source models into memory simultaneously during paramete…