Stars
Deformable DETR: Deformable Transformers for End-to-End Object Detection.
[TPAMI 2022 & CVPR2021 Oral] UP-DETR: Unsupervised Pre-training for Object Detection with Transformers
Papers and Public Datasets for Diabetic Retinopathy Detection
[CIBM] Accurate Leukocyte Detection Based on Deformable-DETR and Multi-Level Feature Fusion for Aiding Diagnosis of Blood Diseases
(MICCAI 2021) Automatic Polyp Segmentation via Multi-scale Subtraction Network
An ultimately comprehensive paper list of Vision Transformer/Attention, including papers, codes, and related websites
Collect some papers about transformer for detection and segmentation. Awesome Detection Transformer for Computer Vision (CV)
Notes + notebooks on EM + variational EM algorithms for Bayesian methods tutorial
🕶 A curated list of Tiny Object Detection papers and related resources.
To be Critical: Self-Calibrated Weakly Supervised Learning for Salient Object Detection
This repository is a clean version of the Object Detection with Semi-Supervised and Self-Supervised Learning project.
(ICME2023) Official implementation of Paper ''Self-supervised Cross-stage Regional Contrastive Learning for Object Detection''
Repository for content related to the final course project of team 14 of the Deep Learning (Spring '22) course at NYU Courant.
This project utilized Self-Supervised learning methods (SimCLR & SimSiam) to recognize vehicle-type (or any object) based on a custom dataset
📷 [ECCV 2022] Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection
[NeurIPS 2023] HASSOD: Hierarchical Adaptive Self-Supervised Object Detection
🌕 [ICCV 2021] Multitask AET with Orthogonal Tangent Regularity for Dark Object Detection. A self-supervised learning way for low-light image object detection.
The repository contains the work done for the project Self/Semi Suprevised Learning for Object Detection as a part of the course Deep Learning offered in Spring 2022 at NYU
A simple tutorial of Variational AutoEncoders with Pytorch
A Variational Autoencoder (VAE) implemented in PyTorch
A Collection of Variational Autoencoders (VAE) in PyTorch.
My implementation of various GAN (generative adversarial networks) architectures like vanilla GAN (Goodfellow et al.), cGAN (Mirza et al.), DCGAN (Radford et al.), etc.
Awesome GAN for Medical Imaging
PyTorch implementations of Generative Adversarial Networks.