Global and Local Prompts Cooperation via Optimal Transport for Federated Learning

[AAAI 2024] FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning

The official pytorch implemention of our CVPR-2024 paper "MMA: Multi-Modal Adapter for Vision-Language Models".

Code for Finetune like you pretrain: Improved finetuning of zero-shot vision models

【CVPR 2023】Learning Federated Visual Prompt in Null Space for MRI Reconstruction

[ECCV2024] The Official Implementation for ''AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection''

Official code of "Generating Instance-level Prompts for Rehearsal-free Continual Learning (ICCV 2023)"

A curated list of awesome prompt/adapter learning methods for vision-language models like CLIP.

StegaStamp of pytorch version

A PyTorch implementation of image steganography utilizing deep convolutional neural networks

Invisible Hyperlinks in Physical Photographs

Invisible Backdoor Attack with Sample-Specific Triggers

[CVPR2024] MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling

A multi-modal CLIP model trained on the medical dataset ROCO

Prompt Learning for Vision-Language Models (IJCV'22, CVPR'22)

The official implementation of CVPR 24' Paper "Learning Transferable Negative Prompts for Out-of-Distribution Detection"

Personalized federated learning codebase for research

❄️🔥 Visual Prompt Tuning [ECCV 2022] https://arxiv.org/abs/2203.12119

This is the official code of the paper "Nuclei Segmentation with Point Annotations from Pathology Images via Self-Supervised Learning and Co-Training"

Segment Anything in Medical Images

[ICLR'24] Heterogeneous Personalized Federated Learning by Local-Global Updates Mixing via Convergence Rate

ICLR 2022, "FedBABU: Toward Enhanced Representation for Federated Image Classification"

The official implantation of SGPT (CVPR2024)

Prov-GigaPath: A whole-slide foundation model for digital pathology from real-world data

NuInsSeg: A Fully Annotated Dataset for Nuclei Instance Segmentation in H&E-Stained Histological Images

[MICCAI2024] "FedFMS: Exploring Federated Foundation Models for Medical Image Segmentation". A framework for fine-tuning SAM (Segment Anything) in the federated learning paradigm for medical image …

[NeurIPS 2023] "FedFed: Feature Distillation against Data Heterogeneity in Federated Learning"