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pro vyhledávání: '"Tian, Aaron Xuxiang"'
Soft prompt tuning is a widely studied parameter-efficient fine-tuning method. However, it has a clear drawback: many soft tokens must be inserted into the input sequences to guarantee downstream performance. As a result, soft prompt tuning is less c
Externí odkaz:
http://arxiv.org/abs/2405.18203
SLAM systems based on Gaussian Splatting have garnered attention due to their capabilities for rapid real-time rendering and high-fidelity mapping. However, current Gaussian Splatting SLAM systems usually struggle with large scene representation and
Externí odkaz:
http://arxiv.org/abs/2405.05702
Autor:
Yang, Chen, He, Yangfan, Tian, Aaron Xuxiang, Chen, Dong, Wang, Jianhui, Shi, Tianyu, Heydarian, Arsalan
In this paper, we introduce a novel approach for autonomous driving trajectory generation by harnessing the complementary strengths of diffusion probabilistic models (a.k.a., diffusion models) and transformers. Our proposed framework, termed the "Wor
Externí odkaz:
http://arxiv.org/abs/2404.02082