利用 GPU 进行标记融合的高光谱图像分类

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Computer Vision and Image Understanding Pub Date : 2024-10-05 DOI:10.1016/j.cviu.2024.104198
He Huang, Sha Tao
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引用次数: 0

摘要

高光谱图像通过光谱数据捕捉物质的细微差别,这对遥感至关重要。变换器已成为应对结构复杂的高维高光谱数据挑战的主流方法。然而,在处理高光谱图像时,它们面临的一个主要挑战是存在大量冗余标记,这会导致计算负荷大幅增加,加重模型的计算负担并影响推理速度。因此,我们根据高光谱图像和纯变压器网络的运行特点,提出了一种令牌融合算法,旨在提高模型的最终精度和吞吐量。令牌融合算法在每个变压器层的注意力机制和多层感知器模块之间引入了令牌合并步骤。在四个高光谱图像数据集上进行的实验表明,我们的标记融合算法无需任何训练即可显著提高推理速度,同时只会导致纯变换器网络的分类准确率略有下降。
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Hyperspectral image classification with token fusion on GPU
Hyperspectral images capture material nuances with spectral data, vital for remote sensing. Transformer has become a mainstream approach for tackling the challenges posed by high-dimensional hyperspectral data with complex structures. However, a major challenge they face when processing hyperspectral images is the presence of a large number of redundant tokens, which leads to a significant increase in computational load, adding to the model’s computational burden and affecting inference speed. Therefore, we propose a token fusion algorithm tailored to the operational characteristics of the hyperspectral image and pure transformer network, aimed at enhancing the final accuracy and throughput of the model. The token fusion algorithm introduces a token merging step between the attention mechanism and the multi-layer perceptron module in each Transformer layer. Experiments on four hyperspectral image datasets demonstrate that our token fusion algorithm can significantly improve inference speed without any training, while only causing a slight decrease in the pure transformer network’s classification accuracy.
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来源期刊
Computer Vision and Image Understanding
Computer Vision and Image Understanding 工程技术-工程:电子与电气
CiteScore
7.80
自引率
4.40%
发文量
112
审稿时长
79 days
期刊介绍: The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views. Research Areas Include: • Theory • Early vision • Data structures and representations • Shape • Range • Motion • Matching and recognition • Architecture and languages • Vision systems
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