Stars
Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution
My implementation of "Patch n’ Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution"
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
Fast and memory-efficient exact attention
Robust machine learning for responsible AI
Official pytorch implementation of "ConvBoost: Boosting ConvNets for Sensor-based Activity Recognition". (accepted at Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technolo…
A collection of AWESOME things about domain adaptation
Contrastive Self-supervised Sequential Recommendation with Robust Augmentation
[ICCV-2023] EMQ: Evolving Training-free Proxies for Automated Mixed Precision Quantization
A professional list of Papers, Tutorials, and Surveys on AI for Time Series in top AI conferences and journals.
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
A collection of papers on the topic of ``Computer Vision in the Wild (CVinW)''
General AI methods for Anything: AnyObject, AnyGeneration, AnyModel, AnyTask, AnyX
An up-to-date & curated list of Awesome IMU-based Human Activity Recognition(Ubiquitous Computing) papers, methods & resources. Please note that most of the collections of researches are mainly bas…
Undergraduate Thesis
Human Activity Recognition, exploring different sensor-types with RealWorld HAR dataset
A collection of timeseries datasets and data loaders for machine learning.
Human activity recognition, is a challenging time series classification task. It involves predicting the movement of a person based on sensor data and traditionally involves deep domain expertise a…
⏰ Collaboratively track worldwide conference deadlines (Website, Python Cli, Wechat Applet) / If you find it useful, please star this project, thanks~
Source codes of the feature aggregation graph convolutional network
Repository for benchmarking graph neural networks (JMLR 2023)