计算机科学
人工智能
计算机视觉
卷积(计算机科学)
鉴定(生物学)
特征提取
特征(语言学)
融合
变压器
模式识别(心理学)
人工神经网络
电压
工程类
语言学
哲学
植物
电气工程
生物
作者
Rui Gong,Xue Zhang,Jianan Pan,Jie Guo,Xiushan Nie
出处
期刊:IEEE MultiMedia
[Institute of Electrical and Electronics Engineers]
日期:2024-04-01
卷期号:31 (2): 61-68
标识
DOI:10.1109/mmul.2024.3398189
摘要
Currently, surveillance cameras are extensively employed in public security, and vehicle re-identification has emerged as a burgeoning research area in computer vision. Nevertheless, vehicle re-identification grapples with the challenges of low intra-class similarity and high inter-class similarity. This study tackles these challenges by introducing a novel vehicle re-identification method that integrates convolution and vision transformer features. Specifically, channel-by-channel convolution is incorporated into the feedforward layer to bolster the extraction of local features. Concurrently, the information from the last layer's class token and other patches is fused to yield a comprehensive and rich featured representation. Experiments conducted on the VeRi776 and VehicleID datasets validate that the proposed method outperforms current state-of-the-art vehicle re-identification methods.
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