- Montreal, Canada
- https://mosymosy.github.io/
Highlights
- Pro
Stars
[NeurIPS 2024] WATT: Weight Average Test-Time Adaptation of CLIP
(Best Paper Awar-MedAGI) Boosting Vision Language Models for Histopathology Classification
ICLR2024 Spotlight: curation/training code, metadata, distribution and pre-trained models for MetaCLIP; CVPR 2024: MoDE: CLIP Data Experts via Clustering
The official GitHub page for the survey paper "A Survey on Data Augmentation in Large Model Era"
[WACV 2025] FDS: Feedback-guided Domain Synthesis with Multi-Source Conditional Diffusion Models for Domain Generalization
MosyMosy / WATT
Forked from Mehrdad-Noori/WATTWATT: Weight Average Test-Time Adaption of CLIP
CAGNet: Content-Aware Guidance for Salient Object Detection
Attention-Guided Version of 2D UNet for Automatic Brain Tumor Segmentation
[PR 2024] TFS-ViT: Token-Level Feature Stylization for Domain Generalization
An Evaluation Toolbox for Salient Object Detection
Structure-Aware Feature Stylization for Domain Generalization
⏰ AI conference deadline countdowns
Mehrdad-Noori / FewShot-CLIP-Strong-Baseline
Forked from FereshteShakeri/FewShot-CLIP-Strong-BaselineReal Spherical Harmonics for PyTorch
Official Pytorch implementation for the paper "MaskLRF: Self-supervised Pretraining via Masked Autoencoding of Local Reference Frames for Rotation-invariant 3D Point Set Analysis"
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (V…
Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization
A collection of AWESOME things about domian adaptation
Torch implementation of neural style algorithm
Utilities for building and using the CEM dataset for unsupervised pre-training and downstream tasks.
LibFewShot: A Comprehensive Library for Few-shot Learning. TPAMI 2023.
Multi-Joint dynamics with Contact. A general purpose physics simulator.
Implementation of Panoptic Segformer, in Pytorch
Corruption and Perturbation Robustness (ICLR 2019)