Vision Transformers and Large-Scale Models

Chapter 35: Foundations of Vision Transformers (ViT, DeiT, patch embeddings, self-attention for images, training challenges) Chapter 36: Hierarchical Vision Transformers (Swin Transformer, Twins, PVT, Nested ViT, hierarchical design principles) Chapter 37: Vision Transformers for Object Detection (DETR, Deformable DETR, DINO, YOLOS, ViTDet) Chapter 38: Vision Transformers for Segmentation (SegFormer, Mask2Former, SETR, Swin-Unet, Segmenter) Chapter 39: Vision Transformers for Video and Temporal Tasks (Video Swin Transformer, TimeSformer, ViViT, MViT) Chapter 40: Hybrid CNN-Transformer Architectures (ConvNeXt, CoAtNet, LeViT, CvT, BoTNet) Chapter 41: Vision Large Language Models (vLLMs) (Flamingo, BLIP, LLaVA, CLIP-ViT, GIT, visual reasoning, image-text alignment) Chapter 42: Scaling and Optimizing Vision Transformers (Efficient ViTs, Sparse Transformers, Long-Range ViTs, FlashAttention for ViTs) Chapter 43: Task-Specific ViT Innovations (ViTPose, TransReID, ViTGAN, ViT-based OCR)