🦜🔗 The platform for reliable agents.

Inference code for Llama models

Let us control diffusion models!

End-to-End Object Detection with Transformers

Implementation of Denoising Diffusion Probabilistic Model in Pytorch

A collection of AWESOME things about domain adaptation

T2I-Adapter

LLaMA: Open and Efficient Foundation Language Models

A library for differentiable nonlinear optimization

Official implementation of "Composer: Creative and Controllable Image Synthesis with Composable Conditions"

PyTorch implementation of Multi-Label Image Recognition with Graph Convolutional Networks, CVPR 2019.

Collection of awesome test-time (domain/batch/instance) adaptation methods

WACV 2020 "Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison"

Official PyTorch implementation of ODISE: Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models [CVPR 2023 Highlight]

Graph Convolutional Networks (GCNs)

Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition in CVPR19

Skeleton-based Action Recognition

Erasing Concepts from Diffusion Models

Pytorch implementation of SIREN - Implicit Neural Representations with Periodic Activation Function

[CVPR23] Official Implementation of MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation

repository for Universal Domain Adaptation through Self-supervision

[CVPR2021] Domain Consensus Clustering for Universal Domain Adaptation

code for our CVPR 2022 paper "DINE: Domain Adaptation from Single and Multiple Black-box Predictors"

papers on sign language recognition and related fields

[CVPR 2023] Learning with Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning

Code for our NeurIPS 2022 (spotlight) paper 'Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation'

The Norm Must Go On: Dynamic Unsupervised Domain Adaptation by Normalization (CVPR 2022)

Official Implementation of LADS (Latent Augmentation using Domain descriptionS)

Pedagogical codebase for a simplified score-based generative model design, with training loop

Normalizing flows in PyTorch