# Closing Editorial for Computer Vision and Pattern Recognition Based on Deep Learning
## 4 Recognition  
[PERSON] et al. [15] proposed an innovative framework merging advanced Optical Character Recognition (OCR) and object detection (OD) technologies for automating visual inspection processes in industrial settings. The system enhances industrial workflows by extracting supplementary information such as barcodes and QR codes, thus reducing manual labor demands and revolutionizing industrial processes.  
[PERSON] et al. [16] proposed a novel approach for accurate temporal modeling in action recognition, employing a Local Spatiotemporal Extraction module (LSTE) and a Channel Time Excitation module (CTE) to capture temporal information in video sequences. Experimental results on the Something-Something V1 and V2 datasets demonstrate the effectiveness of the proposed approach.  
[PERSON] et al. [17] proposed Bina, a specialized Optical Character Recognition (OCR) framework tailored for Persian text, addressing challenges such as character continuity, semicircles, dots, and oblique characters. Bina employs Convolutional Neural Network (CNN) and deep bidirectional Long-Short Term Memory (BLSTM) networks to handle the complexities of Persian text.  
[PERSON] et al. [18] introduced a fast gunshot-type recognition method based on knowledge distillation to address the challenges of large model size and insufficient real-time detection in urban combat scenarios. It preprocesses muzzle blast and shock wave signals, enhancing dataset quality with corresponding Log-Mel spectra. A teacher network, composed of 10 two-dimensional residual modules, and a student network, employing depth-wise separable convolution, are constructed.  
[PERSON] et al. [19] introduced a variable rate IndRNN network to address the challenge of different sampling rates in skeleton-based action recognition, leveraging the well-behaved gradient backpropagation through time of IndRNN by processing samples with variable lengths and time steps, and implementing a learning rate adjustment method based on gradient behavior.  
[PERSON] et al. [20] introduced DA-IndRNN, a novel attention-based deep learning model for skeleton-based action recognition, integrating a deep IndRNN for feature extraction and a multi-layered attention network for reliable attention weight estimation, trained with a new triplet loss function to guide attention learning across different action categories.  
[PERSON] and [PERSON] [21] introduced a traffic light detection and recognition method leveraging YOLOv5s for target detection and AlexNet for image classification, addressing issues of lower accuracy and limited detection types. It enhances recognition rates for small targets and optimizes datasets using the ZeroDCE low-light enhancement algorithm.  
[PERSON] et al. [22] proposed a method for improving the accuracy of environmental sound recognition by employing multi-feature parameters and a time-frequency attention module. The approach begins with pretreatment using multi-feature parameters to extract sound, enhancing input feature expressiveness by supplementing lost phase information from the Log-Mel spectrogram. A time-frequency attention module with multiple convolutions is then employed to extract attention weights from the input feature spectrogram, reducing interference from background noise and irrelevant frequency bands.