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Cognitive resilience: Unraveling the proficiency of image-captioning models to interpret masked visual content
This study explores the ability of Image Captioning (IC) models to decode masked visual content sourced from diverse datasets.
LAMPER: LanguAge Model and Prompt EngineeRing for zero-shot time series classification
This study constructs the LanguAge Model with Prompt EngineeRing (LAMPER) framework, designed to systematically evaluate the adaptability of pre-trained language models (PLMs) in accommodating diverse prompts and their integration in zero-shot time series (TS) classification.
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