A microframework on top of PyTorch with first-class citizen APIs for foundation model adaptation

[ICLR'24] Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Unofficial implementation of the paper "The Chosen One: Consistent Characters in Text-to-Image Diffusion Models"

[ECCV 2024] Improving 2D Feature Representations by 3D-Aware Fine-Tuning

[CVPR'24] NeRF On-the-go: Exploiting Uncertainty for Distractor-free NeRFs in the Wild

Welcome to the project repository for POPE (Promptable Pose Estimation), a state-of-the-art technique for 6-DoF pose estimation of any object in any scene using a single reference.

A cli program of image retrieval using dinov2

[NeurIPS'24] A Simple Image Segmentation Framework via In-Context Examples

Official implementation of the paper 'Exploring Robust Features for Few-Shot Object Detection in Satellite Imagery'

Official repository of the paper "Talking to DINO: Bridging Self-Supervised Vision Backbones with Language for Open-Vocabulary Segmentation"

The inference of DINOv2 ONNX models using the ONNXRuntime library.

This project is an image retrieval system based on DINOv2 and CLIP models, supporting both image-to-image and text-to-image retrieval. Users can upload an image or input text description to retrieve similar images from a predefined image database.

DINOv2 module for use with Autodistill.

An open-source implementaion for fine-tuning DINOv2 by Meta.

The official source code of paper in ECCV 2024. "Aligning Neuronal Coding of Dynamic Visual Scenes with Foundation Vision Models"

This is a warehouse for DinoV2-models, based pytorch framework.

This repository contains the code implementation used in the paper: "Human-in-the-Loop Segmentation of Multi-species Coral Imagery".

Code for my Master's Thesis "Deep Neural Encoding Models of the Human Visual Cortex to Predict fMRI Responses to Natural Visual Scenes" and my submission for the "Algonauts Project 2023 Challenge".

An application for automatic road damage assessment using semantic segmentation on high-resolution images. The project helps municipal authorities and maintenance teams detect and prioritize road repairs, improving safety and reducing costs.

A machine learning project designed to detect road damage from images, leveraging deep learning and computer vision techniques for efficient and accurate damage detection.