MTLSegFormer：基于 Transformer 的多任务学习在精准农业语义分割中的应用（英文版）

英文标题：MTLSegFormer: Multi-task Learning with Transformers for Semantic Segmentation in Precision Agriculture中文摘要：MTLSegFormer 是一种结合多任务学习和注意机制的语义分割方法，通过学习任务相关特征和视觉注意力方法，实现了跨任务信息交换与加权，可以显著提高受其他任务相关性影响较大的任务的准确性。英文摘要：Multi-task learning has proven to be effective in improving the performanceof correlated tasks. Most of the existing methods use a backbone to extractinitial features with independent branches for each task, and the exchange ofinformation between the branches usually occurs through the