###### Abstract  
Raw image classification datasets generally maintain a long-tailed distribution in the real world. Standard classification algorithms face a substantial issue because many labels only relate to a few categories. The model learning processes will tend toward the dominant labels under the influence of their loss functions. Existing systems typically use two stages to improve performance: pretraining on initial imbalanced datasets and fine-tuning on balanced datasets via re-sampling or logit adjustment. These have achieved promising results. However, their limited self-supervised information makes it challenging to transfer such systems to other vision tasks, such as detection and segmentation. Using large-scale contrastive visual-language pretraining, the Open AI team discovered a novel visual recognition method. We provide a simple one-stage model called the text-to-image network (TIN) for long-tailed recognition (LTR) based on the similarities between textual and visual features. The TIN has the following advantages over existing techniques: (1) Our model incorporates textual and visual semantic information. (2) This end-to-end strategy achieves good results with fewer image samples and no secondary training. (3) By using seesaw loss, we further reduce the loss gap between the head category and the tail category. These adjustments encourage large relative magnitudes between the logarithms of rare and dominant labels. TIN conducted extensive comparative experiments with a large number of advanced models on ImageNet-LT, the largest long-tailed public dataset, and achieved the state-of-the-art for a single-stage model with 72.8% at Top-1 accuracy. unmanned aerial vehicle; state grid; transmission line clamps; image classification; multimodule fusion; neural network; long-tailed-recognition Article  
End-to-End: A Simple Template for the Long-Tailed-Recognition of Transmission Line Clamps Via a Vision-Language Model  
Fei  
Vision-based deep learning models have made remarkable progress in recent years in areas including object detection [6; 7; 8], semantic segmentation [9; 10], and image classification [11; 12; 13]. However, long-tailed recognition (LTR), as a classification branch, has long puzzled scholars [14]. As classification problems in the real world tend to exhibit long-tailed imbalanced distributions, most labels are associated with only a few samples [15; 16]. Models trained on these datasets accrue more gradients and slant the outcomes toward dominant labels, resulting in poor results belonging to fewer sample categories. Compared with the high cost of creating a more balanced dataset (e.g., MSCOCO [17] or ImageNet [18]) with manual annotation, it is more cost-effective to improve the model being utilized. After much effort, this problem has been alleviated through techniques such as resampling the training data [19; 20; 21; 22; 23; 24; 25; 26], reweighting gradients [27; 28; 29], loss function replacement [30; 31; 32], transfer learning [33; 34], and data augmentation [35; 36; 37; 38; 39; 40; 41; 42]. Thus, weight normalization relies on the use of more minor weight norms for rare classes, which are also sensitive to the chosen optimizer. However, resampling forcibly destroys the original data's distribution to induce a more precise fit, resulting in suboptimal solutions in real-world settings. Decoupled training is the most common and successful method at present [43; 44]. In the first stage, researchers often conduct pretraining and extract features from the original dataset, and then train the chosen classifier using the above strategies in the second stage. This method is not elegant and requires considerable computational power for two consecutive training steps. The effect of this method still falls short in LTR. Significant research has determined that the current model-based methods primarily focus on the visual module and ignore the inherent relationship between text and visuals. Providing a text information supervision signal for an inadequate dataset could be beneficial.