💳 Integrate Alignet payment service with Magento 2 for seamless and efficient online transactions, enhancing your e-commerce experience.

All-in-one training for vision models (YOLO, ViTs, RT-DETR, DINOv3): pretraining, fine-tuning, distillation.

[RAL] ColonAdapter: Geometry Estimation Through Foundation Model Adaptation for Colonoscopy

The officieal implementation of the paper: CoWS: Self-Supervised Representation Pre-Training for Cross-Machine Fault Diagnosis

[ECCV2024] Video Foundation Models & Data for Multimodal Understanding

[MICCAI 2025] This is the official code for the paper "Lightweight Data-Free Denoising for Detail-Preserving Biomedical Image Restoration"

Code release for "UnSAMv2: Self-Supervised Learning Enables Segment Anything at Any Granularity"

Multimodal classification with CLIP and SimCLR under limited labels and missing modality conditions.

Overcoming the Identity Mapping Problem in Self-Supervised Hyperspectral Anomaly Detection

[ECCV 2024] This is the official code for the paper "Fast Context-Based Low-Light Image Enhancement via Neural Implicit Representations"

This is the implementation of DCMVC: Dual Contrastive Multi-view Clustering, published in Neurocomputing 2025.

[ICCV 2023] Self-supervised Semantic Segmentation: Consistency over Transformation

[MICCAI'23] Foundation Model for Endoscopy Video Analysis via Large-scale Self-supervised Pre-train

Official implementation of "Augmentation-aware Self-supervised Learning with Conditioned Projector"

Self-supervised denoising for Monte Carlo Rendering

[AISY] Self-Supervised Learning-Based Framework for Speckle Reduction of Optical Coherence Tomography Images Using Frame Interpolation

[ICCV 2025] Official PyTorch Implementation of "Learning Self-supervised Part-aware 3D Hybrid Representations of 2D Gaussians and Superquadrics"

TomoSelfDEQ: Self-Supervised Deep Equilibrium Learning for Sparse-Angle CT Reconstruction (2025)

Self-supervised models for encoding protein localization patterns from microscopy images

Self-supervised decorrelation-denoising of X-ray radiographs