1 citations · 1 across the 1 of their papers we have counts for
5 papers
Domain-Invariant Prompt Learning for Vision-Language Models
Arsham Gholamzadeh Khoee, Yinan Yu, Robert Feldt
Large pre-trained vision-language models like CLIP have transformed computer vision by aligning images and text in a shared feature space, enabling robust zero-shot transfer via pr…
GateLens: A Reasoning-Enhanced LLM Agent for Automotive Software Release Analytics
Arsham Gholamzadeh Khoee, Shuai Wang, Robert Feldt +2
Ensuring reliable data-driven decisions is crucial in domains where analytical accuracy directly impacts safety, compliance, or operational outcomes. Decision support in such domai…
Latent Domain Prompt Learning for Vision-Language Models
Zhixing Li, Arsham Gholamzadeh Khoee, Yinan Yu
The objective of domain generalization (DG) is to enable models to be robust against domain shift. DG is crucial for deploying vision-language models (VLMs) in real-world applicati…
GoNoGo: An Efficient LLM-based Multi-Agent System for Streamlining Automotive Software Release Decision-Making
Arsham Gholamzadeh Khoee, Yinan Yu, Robert Feldt +3
Traditional methods for making software deployment decisions in the automotive industry typically rely on manual analysis of tabular software test data. These methods often lead to…
Domain Generalization through Meta-Learning: A Survey
Arsham Gholamzadeh Khoee, Yinan Yu, Robert Feldt
Deep neural networks (DNNs) have revolutionized artificial intelligence but often lack performance when faced with out-of-distribution (OOD) data, a common scenario due to the inev…