A library for differentiable nonlinear optimization

Autoregressive Model Beats Diffusion: 🦙 Llama for Scalable Image Generation

A unified framework for 3D content generation.

Python script for Blender for generating images of Greebles in random poses and lighting conditions.

MLNLP社区用来帮助大家避免论文投稿小错误的整理仓库。 Paper Writing Tips

[CVPR 2022] Official repository of AdaFocusV2.

A Harder ImageNet Test Set (CVPR 2021)

EntitySeg Toolbox: Towards Open-World and High-Quality Image Segmentation

AzureML customer managed k8s compute samples

arXiv LaTeX Cleaner: Easily clean the LaTeX code of your paper to submit to arXiv

Reducing spatial redundancy in video recognition. SOTA computational efficiency.

Automated Machine Learning with scikit-learn

A collection of loss functions for medical image segmentation

CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image

Is a geometric model required to synthesize novel views from a single image?

Audio visualizer and effects engine for RGB keyboards, mice, and accessories using the OpenRGB SDK. Supports Windows, Linux, and MacOS. Issue tracker on GitLab (https://gitlab.com/CalcProgrammer1/K…

Official implementation of "An Image is Worth 16x16 Words, What is a Video Worth?" (2021 paper)

Code release for "Towards Better Generalization: Joint Depth-Pose Learning without PoseNet".

Human Trajectory Prediction Dataset Benchmark (ACCV 2020)

🎥 Matplotlib animations made easy

A lightweight, dependency-free Python library (and command-line utility) for downloading YouTube Videos.

Repository for "Space-Time Correspondence as a Contrastive Random Walk" (NeurIPS 2020)

Code release for NeRF (Neural Radiance Fields)

Code for Learnt Stereo Machines based on the NIPS 2017 paper

(ICRA) Anytime Stereo Image Depth Estimation on Mobile Devices

Virginia Tech Vision and Learning Reading Group

PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume, CVPR 2018 (Oral)

Joint-task Self-supervised Learning for Temporal Correspondence (NeurIPS 2019)

Understanding Deep Networks via Extremal Perturbations and Smooth Masks