Low-Resolution Self-Attention for Semantic Segmentation

doi:doi:10.57702/znjcgqn3

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Low-Resolution Self-Attention for Semantic Segmentation

Semantic segmentation tasks naturally require high-resolution information for pixel-wise segmentation and global context information for class prediction.

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The json representation of the dataset with its distributions based on DCAT.
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Cite this as

Yu-Huan Wu, Shi-Chen Zhang, Yun Liu, Le Zhang, Xin Zhan, Daquan Zhou, Jiashi Feng, Ming-Ming Cheng, Liangli Zhen (2024). Dataset: Low-Resolution Self-Attention for Semantic Segmentation. https://doi.org/10.57702/znjcgqn3

DOI retrieved: December 2, 2024

Additional Info

Field	Value
Created	December 2, 2024
Last update	December 2, 2024
Author	Yu-Huan Wu
More Authors	Shi-Chen Zhang Yun Liu Le Zhang Xin Zhan Daquan Zhou Jiashi Feng Ming-Ming Cheng Liangli Zhen
Homepage	https://arxiv.org/abs/2106.09543