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University of Copenhagen
- @Abe_404
- https://scholar.google.dk/citations?hl=en&user=RsZFz_IAAAAJ&view_op=list_works&sortby=pubdate
Stars
Simple Online Realtime Tracking with a Deep Association Metric
[CVPR 2023 Highlight] InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions
[AAAI 2025] Official PyTorch implementation of "TinySAM: Pushing the Envelope for Efficient Segment Anything Model"
[ICLR'23] AIM: Adapting Image Models for Efficient Video Action Recognition
Segment Anything in High Quality [NeurIPS 2023]
[MICCAI 2024] The official repository for DeSAM: Decoupled Segment Anything Model for Generalizable Medical Image Segmentation.
This is an official repo for fine-tuning SAM to customized medical images.
Exploration of the latent space of generative models on Lung-CT scans
A modern, C++-native, test framework for unit-tests, TDD and BDD - using C++14, C++17 and later (C++11 support is in v2.x branch, and C++03 on the Catch1.x branch)
A latent text-to-image diffusion model
Mattermost is an open source platform for secure collaboration across the entire software development lifecycle..
Dictdiffer is a module that helps you to diff and patch dictionaries.
OpenMMLab Semantic Segmentation Toolbox and Benchmark.
This is the official code for MobileSAM project that makes SAM lightweight for mobile applications and beyond!
Tracking and collecting papers/projects/others related to Segment Anything.
Fine-tune SAM (Segment Anything Model) for computer vision tasks such as semantic segmentation, matting, detection ... in specific scenarios
PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO
End-to-End Object Detection with Transformers
Code and Data for the paper 'LLM Cognitive Judgements Differ From Human'
Gradually-Warmup Learning Rate Scheduler for PyTorch
[CVPR 2023] Official implementation of the paper "Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation"
pytest plugin for Qt (PyQt5/PyQt6 and PySide2/PySide6) application testing