Due to the lack of a specific design for scenarios such as scale change, illumination difference, and occlusion, current person re-identification methods are difficult to put into practice. A Multi-Branch Feature Fusion Network (MFFNet) is proposed, and Shallow Feature Extraction (SFF) and Multi-scale Feature Fusion (MFF) are utilized to obtain robust global feature representations while leveraging the Hybrid Attention Module (HAM) and Anti-erasure Federated Block Network (AFBN) to solve the problems of scale change, illumination difference and occlusion in scenes. Finally, multiple loss functions are used to efficiently converge the model parameters and enhance the information interaction between the branches. The experimental results show that our method achieves significant improvements over Market-1501, DukeMTMC-reID, and MSMT17. Especially on the MSMT17 dataset, which is close to real-world scenarios, MFFNet improves by 1.3 and 1.8% on Rank-1 and mAP, respectively.

The rich information and complex background of industrial images make it a challenging task to improve the high compression rate of images. Current learning-based image compression methods mostly use customized convolutional neural networks (CNNs), which find it difficult to cope with the complex production background of industrial images. This causes useful information to be lost in the abundance of irrelevant data, making it difficult to accurately extract important features during the feature extraction stage. To address this, a Multi-path Residual Asymmetric Convolutional Compression Network (MRACNN) is proposed. Firstly, a Multi-path Residual Asymmetric Convolution Block (MRACB) is introduced, which includes the Multi-path Residual Asymmetric Convolution Down-sampling Module for down-sampling in the encoder to extract key features, and the Mult-path Residual Asymmetric Convolution Up-sampling Module for up-sampling in the decoder to recover details and reconstruct the image. This feature transfer and information flow enables the better capture of image details and important information, thereby improving the quality and efficiency of image compression and decompression. Furthermore, a two-branch enhanced local attention mechanisms, and a channel-squeezing entropy model based on the compression-based enhanced local attention module is proposed to enhance the performance of the modeled compression. Extensive experimental evaluations demonstrate that the proposed method outperforms state-of-the-art techniques, achieves superior Rate–Distortion Performance, and excels in preserving local details.