A

ADE20k

Dense Prediction by Means of Self-attention Layers - research of models for dense prediction (semantic segmentation) - primarily transformers (models in focus: Segmenter, Swin transformer) and comparison with convolutional models (model in focus: pyramidal SwiftNet). Also, research, design and implementation of pyramidal models of transformer-convolutional model (Segmenter-SwiftNet) and transformer-transformer (Segmenter-Segmenter) type. Implementation is in PyTorch deep learning framework. My graduate thesis computer vision project.