score_sde_pytorch PyTorch implementation for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral). This repo contains a PyTorch implementation for the paper Score-Based Generative Modeling through Stochastic Differential Equations by Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole What does this code do? Aside from the NCSN++ and DDPM++ models in our paper, this codebase also re-implements many previous score-based models in one place, including NCSN from Generative Modeling by Estimating Gradients of […]

MI-AOD This is the code for Multiple Instance Active Learning for Object Detection, CVPR 2021. In this paper, we propose Multiple Instance Active Object Detection (MI-AOD), to select the most informative images for detector training by observing instance-level uncertainty. MI-AOD defines an instance uncertainty learning module, which leverages the discrepancy of two adversarial instance classifiers trained on the labeled set to predict instance uncertainty of the unlabeled set. MI-AOD treats unlabeled images as instance bags and feature anchors in images […]

RfD-Net Yinyu Nie, Ji Hou, Xiaoguang Han, Matthias NießnerIn CVPR, 2021. From an incomplete point cloud of a 3D scene (left), our method learns to jointly understand the 3D objects and reconstruct instance meshes as the output (right). Install This implementation uses Python 3.6, Pytorch1.7.1, cudatoolkit 11.0. We recommend to use conda to deploy the environment. conda env create -f environment.yml conda activate rfdnet pip install -r requirements.txt Next, compile the external libraries by python setup.py build_ext –inplace Install PointNet++ […]