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DIPY is the paragon 3D/4D+ imaging library in Python. Contains generic methods for spatial normalization, signal processing, machine learning, statistical analysis and visualization of medical images. Additionally, it contains specialized methods for computational anatomy including diffusion, perfusion and structural imaging.

All-in-one Toolbox for Computer Vision Research.

Cvpr 2023 Papers ⭐ 185

CVPR 2023 Papers: Dive into advanced research presented at the leading computer vision conference. Keep up to date with the latest developments in computer vision and deep learning. Code included. A star in the development of visual intelligence!

[CVPR 2020] Action Segmentation with Joint Self-Supervised Temporal Domain Adaptation (PyTorch)

Conditional Motion Propagation ⭐ 99

Code for our CVPR 2019 work.

Open source Python library for building bioimage analysis pipelines

Awesome Vision Transformer Collection ⭐ 81

Variants of Vision Transformer and its downstream tasks

Nerf Sos ⭐ 64

[ICLR2023] "NeRF-SOS: Any-View Self-supervised Object Segmentation from Complex Real-World Scenes", Zhiwen Fan, Peihao Wang, Xinyu Gong, Yifan Jiang, Dejia Xu, Zhangyang Wang

Awesome Computervision ⭐ 60

Awesome-ComputerVision

Decoder Denoising ⭐ 27

Pytorch reimplementation of Decoder Denoising Pretraining for Semantic Segmentation

Ccml_learning ⭐ 25

This is the offical website for paper ''Category-consistent deep network learning for accurate vehicle logo recognition''

This repo contains the code for the CVPR 2023 paper: "CrOC : Cross-View Online Clustering for Dense Visual Representation Learning".

Lighter is a PyTorch/Pytorch Lightning framework for streamlining deep learning experiments though configuration files.

BT-Unet: A self-supervised learning framework

Morton Net ⭐ 9

PyTorch implementation to train MortonNet and use it to compute point features. MortonNet is trained in a self-supervised fashion, and the features can be used for general tasks like part or semantic segmentation of point clouds.

[NeurIPS'23] DropPos: Pre-Training Vision Transformers by Reconstructing Dropped Positions

Annotation Free Fewshot ⭐ 6

[MICCAI 2023] Self-supervised Few-shot Learning for Semantic Segmentation: An Annotation-free Approach

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