Pauljanson002
PhD student @ Mila Quebec and Concordia university, Montreal. Interested in Deep learning and Computer Vision research.
Mila Quebec , Concordia UniversityMontreal , Quebec
Pauljanson002's Stars
facebookresearch/segment-anything
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.
microsoft/DeepSpeed
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
Vision-CAIR/MiniGPT-4
Open-sourced codes for MiniGPT-4 and MiniGPT-v2 (https://minigpt-4.github.io, https://minigpt-v2.github.io/)
Stability-AI/generative-models
Generative Models by Stability AI
hpcaitech/Open-Sora
Open-Sora: Democratizing Efficient Video Production for All
voxel51/fiftyone
Refine high-quality datasets and visual AI models
facebookresearch/hydra
Hydra is a framework for elegantly configuring complex applications
encoredev/encore
Open Source Development Platform for building robust type-safe distributed systems with declarative infrastructure
NVIDIAGameWorks/kaolin
A PyTorch Library for Accelerating 3D Deep Learning Research
dreamgaussian/dreamgaussian
[ICLR 2024 Oral] Generative Gaussian Splatting for Efficient 3D Content Creation
andrewekhalel/MLQuestions
Machine Learning and Computer Vision Engineer - Technical Interview Questions
instill-ai/instill-core
🔮 Instill Core is a full-stack AI infrastructure tool for data, model and pipeline orchestration, designed to streamline every aspect of building versatile AI-first applications
omerbt/TokenFlow
Official Pytorch Implementation for "TokenFlow: Consistent Diffusion Features for Consistent Video Editing" presenting "TokenFlow" (ICLR 2024)
Anything-of-anything/Anything-3D
Segment-Anything + 3D. Let's lift anything to 3D.
Sroy20/machine-learning-interview-questions
This repository is to prepare for Machine Learning interviews.
facebookresearch/co3d
Tooling for the Common Objects In 3D dataset.
google/learned_optimization
diffusion-classifier/diffusion-classifier
Diffusion Classifier leverages pretrained diffusion models to perform zero-shot classification without additional training
yael-vinker/live_sketch
Mathux/TEMOS
Official PyTorch implementation of the paper "TEMOS: Generating diverse human motions from textual descriptions", ECCV 2022 (Oral)
Madaoer/S3IM-Neural-Fields
[ICCV 2023] Pytorch implementation of "S3IM: Stochastic Structural SIMilarity and Its Unreasonable Effectiveness for Neural Fields".
JH-LEE-KR/l2p-pytorch
PyTorch Implementation of Learning to Prompt (L2P) for Continual Learning @ CVPR22
tovacinni/awesome-3d-compression
Papers and such on 3D data compression and streaming
JH-LEE-KR/dualprompt-pytorch
PyTorch Implementation of DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning @ ECCV22
eric-ai-lab/PEViT
Official implementation of AAAI 2023 paper "Parameter-efficient Model Adaptation for Vision Transformers"
aminebdj/3D-OWIS
[NeurIPS2023] 3D-OWIS is capable of detecting unknown instances in inference, and progressively learning novel classes in the process of training.
Tangshitao/NeuMap
NeuMap: Neural Coordinate Mapping by Auto-Transdecoder for Camera Localization, CVPR2023
wx-zhang/IGCZSL
bentherien/mu_learned_optimization
IljaAvadiev/generative
Notebooks and scripts for generative models