Semantic Enhancement Loss Function Based on Attention Mechanism

Teng Shuhua, Zheng Lidong, Cheng Zhengting, Yuan Zhian, Ma Yanxin
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Abstract

Panoramic segmentation is an important research direction in computer vision. Considering that different applications have different requirements for semantic segmentation accuracy, a semantic enhancement loss function based on attention mechanism is proposed. By adding attention mechanism, it can enhance the sensitivity to the semantic information of task attention and improve the classification accuracy of specific objects and backgrounds. The experimental results show that the semantic enhancement loss function can effectively improve the classification accuracy of semantic categories required by tasks.
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基于注意机制的语义增强损失函数
全景分割是计算机视觉中的一个重要研究方向。针对不同应用对语义切分精度的要求不同,提出了一种基于注意机制的语义增强损失函数。通过添加注意机制,可以增强对任务注意语义信息的敏感性,提高对特定对象和背景的分类准确率。实验结果表明,语义增强损失函数可以有效地提高任务所需语义类别的分类精度。
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