# YOLO-SE: Improved YOLOv8 for Remote Sensing Object Detection and Recognition
## 2 Related Work
### Vision Transformer  
The transformer [30] was initially devised for machine translation tasks within the realm of natural language processing (NLP). Owing to its potent representation capabilities, researchers have been investigating avenues to leverage transformers for computer vision tasks. Models based on transformers have demonstrated performance on diverse visual benchmarks comparable to or surpassing other network types such as convolutional and recurrent neural networks. The growing attention within the computer vision community toward transformers is attributed to their superior performance and reduced need for domain-specific feature engineering [31].  
[PERSON] et al. [32] conducted training on a sequence transformer for pixel-level regression, achieving results akin to CNNs in image-classification tasks. Another notable model, ViT, directly applies pure transformers to image patch sequences, enabling the classification of entire images. [PERSON] et al. [33] recently introduced a model that attained state-of-the-art performance across various image recognition benchmarks.  
While traditional visual transformers excel in capturing long-range dependencies between patches, they often neglect local feature extraction and project 2D patches onto vectors, using simple linear layers. In response to this, recent research has concentrated on enhancing modeling capacity for local information. TNT [34], for instance, divides patches into multiple sub-patches and introduces a novel transformer-in-transformer architecture. This leverages inner transformer blocks to model relationships between sub-patches and outer transformer blocks for patch-level information exchange. Twins [35] and CAT [36] alternatively perform local and global attention at different layers. Swin transformers [37] execute local attention within a window and introduce a shift-window partition method for cross-window connections. RegionViT [38] generates region tokensand local tokens from images, with local tokens receiving global information through attention to region tokens. Beyond local attention, some research suggests enhancing local information through local feature aggregation, such as T2T. Improved computations for self-attention layers have also garnered attention. DeepViT [39], for instance, proposes building cross-head communication to regenerate attention maps, fostering increased diversity at different levels.  
[PERSON] et al. [40] integrated a transformer prediction head into the YOLOv5 structure, proposing the TPH-YOLOv5 model. This model introduces an additional prediction head to detect objects at different scales, utilizing a transformer prediction head (TPH) to leverage the predictive potential of self-attention mechanisms. Building upon this, [PERSON] et al. [41] enhanced the model with the TPH-YOLOv5++ version, significantly reducing computational costs and improving detection speed. In TPH-YOLOv5++, they introduced a cross-layer asymmetric transformer (CA-Trans) to replace the extra prediction head while maintaining its functionality. The use of the Sparse Local Attention (SLA) module effectively captures asymmetric information between additional heads and other heads, enriching the features of other heads.