
This research paper proposes a novel framework that leverages the power of large language models (LLMs) and layout-to-image synthesis (LIS) to significantly improve the performance of few-shot object detection models.


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boosting-few-shot-object-detection-with-large-language-models-and-layout-to-image-synthesis


Boosting Few-Shot Object Detection with Large Language Models and Layout-to-Image Synthesis


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DE-ViTは、微調整不要の新しいアーキテクチャに基づく少数ショット物体検出手法です。


Boosting Few-Shot Object Detection with Large Language Models and Layout-to-Image Synthesis

少ない例で全てを検出する