Implementation of ViViT: A Video Vision Transformer

GitHub repository for the Kvasir-Capsule dataset.

Computational Human Behavior Lab - Hana & Michael

The official pytorch implementation of our paper "Is Space-Time Attention All You Need for Video Understanding?"

In this project, we propose to deal with the data scarcity problem in a specific NLP task by harnessing existing annotated datasets from related tasks. Our approach involves training a multi-head a…

The implementation of the technical report: "Customized Segment Anything Model for Medical Image Segmentation"

A review of papers proposing novel GNN methods with application to brain connectivity published in 2017-2020.

Dynamic Software lets to any user to customize UI and functionality of any open-source software with natural language using GPT, no coding needed!

Official implementation for "Blended Latent Diffusion" [SIGGRAPH 2023]

Segment Anything in Medical Images

Adapting Meta AI's Segment Anything to Downstream Tasks with Adapters and Prompts

[ICLR 2023 Spotlight] Vision Transformer Adapter for Dense Predictions

Automated dense category annotation engine that serves as the initial semantic labeling for the Segment Anything dataset (SA-1B).

PyTorch code and models for the DINOv2 self-supervised learning method.

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

Semi Supervised Learning for Medical Image Segmentation, a collection of literature reviews and code implementations.

Official implementation for the paper "Deep ViT Features as Dense Visual Descriptors".

[CVPR 2022] Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localization

RadImageNet, a pre-trained convolutional neural networks trained solely from medical imaging to be used as the basis of transfer learning for medical imaging applications.

Pytorch Geometric Tutorials

Graph Convolutional Networks in PyTorch

Official Implementation for the "Conffusion: Confidence Intervals for Diffusion Models" paper.