End-to-End Object Detection with Transformers
Introducing DETR, a novel end-to-end object detection framework that leverages Transformers to directly predict a set of object bounding boxes.
Expert analysis and in-depth reviews of machine learning research papers. Covering computer vision, deep learning, and AI innovations with practical insights.
Introducing DETR, a novel end-to-end object detection framework that leverages Transformers to directly predict a set of object bounding boxes.
Introducing BLIP-2, a new vision-language model that leverages frozen image encoders and large language models to achieve improved efficiency and performance in various multimodal tasks.
Introducing Vision Transformer (ViT), a pure transformer architecture for image recognition that achieves state-of-the-art results.
A comprehensive survey of techniques for optimizing the inference phase of transformer networks.
Introducing SURF (Speeded Up Robust Features), a fast and robust algorithm for local feature detection and description, often used in applications like object recognition, image registration, and 3D reconstruction.
Introducing Swin Transformer, a hierarchical Vision Transformer that uses shifted windows to achieve improved efficiency and performance in various vision tasks.