Swin Transformer V2 Explained in 3 Minutes! | Why Attention Had to Evolve Beyond ViT скачать в хорошем качестве

Swin Transformer V2 Explained in 3 Minutes! | Why Attention Had to Evolve Beyond ViT

Transformers changed computer vision forever. But why did we move beyond Vision Transformers? In this video, we explore the evolution from Vision Transformer (ViT) to Swin Transformer and finally Swin Transformer V2, focusing on the real architectural reasons behind each transition. We discuss: ✅ Why global attention in ViT becomes computationally expensive ✅ How shifted window attention makes Swin Transformer efficient ✅ Information mixing across windows ✅ Training instability in deep vision transformers ✅ Cosine attention and temperature scaling ✅ Log-spaced continuous positional bias in Swin Transformer V2 ✅ Scaling transformers to high-resolution images This is a theoretical deep dive explaining how modern vision transformers became scalable and stable for real-world computer vision tasks like detection and segmentation. #visiontransformers #SwinTransformer #SwinV2 #deeplearning #computervision #Transformers #attentionmechanism #machinelearning #airesearch #neuralnetworks #ViT #selfattention #DLTheory #aiexplained #ResearchPaperExplained