Adapting to length shift: Flexilength network for trajectory prediction

EFIN-MP: Explicit Future Interaction Network for Motion Prediction

L Li, J Su, L Qiu, J Lian, G Guo - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Accurate prediction the future movements of surrounding traffic participants is crucial for
autonomous driving. Among various strategies, learning complex interactive behaviors …
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[PDF] arxiv.org

Deciphering Movement: Unified Trajectory Generation Model for Multi-Agent

Y Xu, Y Fu - arXiv preprint arXiv:2405.17680, 2024 - arxiv.org
Understanding multi-agent behavior is critical across various fields. The conventional
approach involves analyzing agent movements through three primary tasks: trajectory …
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[PDF] arxiv.org

Less is More: Pseudo-Label Filtering for Continual Test-Time Adaptation

J Tan, F Lyu, C Ni, T Feng, F Hu, Z Zhang… - arXiv preprint arXiv …, 2024 - arxiv.org
Continual Test-Time Adaptation (CTTA) aims to adapt a pre-trained model to a sequence of
target domains during the test phase without accessing the source data. To adapt to …
被引用次数：1 相关文章 所有 2 个版本

Towards Efficient Deep Learning in Computer Vision via Network Sparsity and Distillation

H Wang - 2024 - search.proquest.com
Artificial intelligence (AI) empowered by deep learning, has been profoundly transforming
the world. However, the excessive size of these models remains a central obstacle that limits …
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