3D U-Net, a powerful deep learning architecture for medical image segmentation, is designed to process 3D volumetric data like brain tumors, enabling a more comprehensive and precise analysis of brain ...
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DETR: Overview and Inference
In the groundbreaking paper “Attention is all you need”, Transformers architecture was introduced for sequence to sequence tasks in NLP. Models like Bert, GPT were built on the top of Transformers ...
YOLO11: Redefining Real-Time Object Detection
YOLO11 is finally here, revealed at the exciting Ultralytics YOLO Vision 2024 (YV24) event. 2024 is a year of YOLO models. After the release of YOLOv8 in 2023, we got YOLOv9 and YOLOv10 this year, and ...
Exploring DINO: Self-Supervised Transformers for Road Segmentation with ResNet50 and U-Net
DINO is a self-supervised learning (SSL) framework that uses the Vision Transformer (ViT) as it's core architecture. While SSL initially gained popularity through its use in natural language ...
Sapiens: Foundation for Human Vision Models by Meta
Sapiens, a family of foundational Human Vision Models by Rawal et al., from Meta, achieves state-of-the-art results for human centric tasks like 2D pose estimation, body-part segmentation, depth ...
ColPali: Enhancing Financial Report Analysis with Multimodal RAG and Gemini
ColPali multimodal RAG offers a novel approach for efficient retrieval of elements such as images, tables, charts, and texts by treating each page as an image. This method takes advantage of Vision ...
