实验室硕士生李章伟撰写的论文《MFENet: Multi-information Feature Enhancement Network for Vehicle Re-identification》被国际学术期刊《IEEE Transactions on Artificial Intelligence》录用。论文将于2025年正式发表。
论文摘要如下：
Abstract—Vehicle re-identification (Re-ID) targets cross-camera image retrieval and is a widely used technology in
intelligent transportation systems. Current Re-ID methods primarily enhance feature extraction by focusing on either global
or local features, but they often fail to effectively leverage diverse information. To address these limitations, we propose
a multi-information feature enhancement network (MFENet) that integrates diverse information types to enhance feature
representation and boost model accuracy. Specifically, (1) a coarse-grained feature enhancement (CFE) module is employed
to remove background influence on image features. This module filters the background, enabling the network model to extract
more accurate vehicle features, such as color and model. (2) A fine-grained feature enhancement (FFE) module collects detailed
information about vehicles by extracting features from subtle areas (e.g., vehicle lights and rearview mirrors) of an image,
providing more unique clues about the vehicle. (3) A latent feature enhancement (LFE) module is designed to mine latent
features and enrich vehicle features using non-visual cues, such as the vehicle’s camera and orientation, without relying on image information. Extensive experiments on vehicle Re-ID datasets demonstrate that MFENet outperforms most existing methods.