Optimizing Large Railway Vision Models for Efficient In-Context Learning

Large railway vision models (LRVMs) have exhibited remarkable performance in tackling diverse railway-related vision-based tasks, attributed to their capacity for in-context learning (ICL). However, in terms of speed and memory usage, these models suffer from inefficient hardware utilization, partic...

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Bibliographic Details
Main Authors: Xuemei Zhan, Xubo Wu, Hua Ma
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/10975287/
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