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pro vyhledávání: '"Li, Jonathan"'
Recently, point cloud processing and analysis have made great progress due to the development of 3D Transformers. However, existing 3D Transformer methods usually are computationally expensive and inefficient due to their huge and redundant attention
Externí odkaz:
http://arxiv.org/abs/2405.15827
3D Transformers have achieved great success in point cloud understanding and representation. However, there is still considerable scope for further development in effective and efficient Transformers for large-scale LiDAR point cloud scene segmentati
Externí odkaz:
http://arxiv.org/abs/2405.15826
3D urban scene reconstruction and modelling is a crucial research area in remote sensing with numerous applications in academia, commerce, industry, and administration. Recent advancements in view synthesis models have facilitated photorealistic 3D r
Externí odkaz:
http://arxiv.org/abs/2405.11021
Wildfires have significant impacts on global vegetation, wildlife, and humans. They destroy plant communities and wildlife habitats and contribute to increased emissions of carbon dioxide, nitrogen oxides, methane, and other pollutants. The predictio
Externí odkaz:
http://arxiv.org/abs/2405.01607
The clinical trial process, also known as drug development, is an indispensable step toward the development of new treatments. The major objective of interventional clinical trials is to assess the safety and effectiveness of drug-based treatment in
Externí odkaz:
http://arxiv.org/abs/2404.13235
This study evaluates the performance of general-purpose AI, like ChatGPT, in legal question-answering tasks, highlighting significant risks to legal professionals and clients. It suggests leveraging foundational models enhanced by domain-specific kno
Externí odkaz:
http://arxiv.org/abs/2404.12349
Autor:
Csaki, Zoltan, Li, Bo, Li, Jonathan, Xu, Qiantong, Pawakapan, Pian, Zhang, Leon, Du, Yun, Zhao, Hengyu, Hu, Changran, Thakker, Urmish
Despite the widespread availability of LLMs, there remains a substantial gap in their capabilities and availability across diverse languages. One approach to address these issues has been to take an existing pre-trained LLM and continue to train it o
Externí odkaz:
http://arxiv.org/abs/2404.05829
The unstructured nature of point clouds demands that local aggregation be adaptive to different local structures. Previous methods meet this by explicitly embedding spatial relations into each aggregation process. Although this coupled approach has b
Externí odkaz:
http://arxiv.org/abs/2308.16532
Autor:
Osco, Lucas Prado, Wu, Qiusheng, de Lemos, Eduardo Lopes, Gonçalves, Wesley Nunes, Ramos, Ana Paula Marques, Li, Jonathan, Junior, José Marcato
Segmentation is an essential step for remote sensing image processing. This study aims to advance the application of the Segment Anything Model (SAM), an innovative image segmentation model by Meta AI, in the field of remote sensing image analysis. S
Externí odkaz:
http://arxiv.org/abs/2306.16623
In the domain of remote sensing image interpretation, road extraction from high-resolution aerial imagery has already been a hot research topic. Although deep CNNs have presented excellent results for semantic segmentation, the efficiency and capabil
Externí odkaz:
http://arxiv.org/abs/2306.04947