Showing 81-100 of 141 projects
A video conversation model that combines LLM capabilities with pretrained visual encoders for video-based chatbots.
A self-supervised learning framework for learning general human representations from unlabeled images.
A one-stop Transformer library for state-of-the-art code language models and AI-powered code understanding.
An open-source library for constructing vectorized high-definition maps for autonomous driving in real-time
Unsupervised image classification library using contrastive learning and SCAN algorithm.
An official implementation of HigherHRNet, a scale-aware human pose estimation model.
A library for self-supervised learning on graphs, providing contrastive, generative, and predictive pretext tasks.
An Electron-based app that provides a visual representation of disk and memory usage for developers.
Unified Training of Universal Time Series Forecasting Transformers for accurate time series forecasting.
Audio Spectrogram Transformer (AST) for audio classification and representation learning tasks.
An open-source library for research on learning disentangled representations.
This is a source code and dataset for an enhanced language representation model ERNIE, focused on using informative entities.
Real-time ASCII representation of webcam video using the getUserMedia API, for a unique display effect.
An open-source implementation of a hierarchical 3D Gaussian representation for rendering large datasets in real-time
An open-source library for learning continuous image representation using local implicit image function.
Pre-trained model for pitch estimation from audio, useful for music information retrieval tasks.
A PyTorch implementation of MotionBERT, a unified approach for learning human motion representations.
PyTorch implementation of the MoCo v3 unsupervised learning algorithm for feature representation.
This is a library for sparse representation and high-resolution 3D shape modeling, useful for computer graphics and vision tasks.
A comprehensive list of contrastive self-supervised learning papers for machine learning researchers.
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