pip install -r requirements.txt python eval.py –dataset_name llff –root_dir /dataset/nerf_llff_data/room –N_importance 64 –img_wh 504 378 –model nerf –ckpt_path room.ckpt –timestamp test Refactoring in progress. @InProceedings{Xu_2022_SinNeRF, author = {Xu, Dejia and Jiang, Yifan and Wang, Peihao and Fan, Zhiwen and Shi, Humphrey and Wang, Zhangyang}, title = {SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image}, journal={arXiv preprint arXiv:2204.00928},    

This repository is the official implementation of Colar. In this work, we study the online action detection and develop an effective and efficient exemplar-consultation mechanism. Paper from arXiv. Requirements To install requirements: conda env create -n env_name -f environment.yaml Before running the code, please activate this conda environment. Data Preparation a. Download pre-extracted features from baiduyun (code:cola) Please ensure the data structure is as below ├── data └── thumos14    

Dynamic MLP, which is parameterized by the learned embeddings of variable locations and dates to help fine-grained image classification. Requirements Experiment Environment python 3.6 pytorch 1.7.1+cu101 torchvision 0.8.2 Get pretrained models for SK-Res2Net following here.Get datasets following here. Train the model 1. Train image-only model Specify –image_only for training image-only models. ResNet-50 (67.924% Top-1 acc) CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3 train.py –name res50_image_only –

This PyTorch framework implements a number of gradient inversion attacks that breach privacy in federated learning scenarios,covering examples with small and large aggregation sizes and examples both vision and text domains. This includes implementations of recent work such as: But also a range of implementations of other attacks from optimization attacks (such as “Inverting Gradients” and “See through Gradients”) to recent analytic and recursive attacks. Jupyter notebook examples for these attacks can be found in the examples/ folder. Overview: This […]

This is the official implementation for the ICCV 2021 paper Learning Signed Distance Field for Multi-view Surface Reconstruction In this work, we introduce a novel neural surface reconstruction framework that leverages the knowledge of stereo matching and feature consistency to optimize the implicit surface representation. More specifically, we apply a signed distance field (SDF) and a surface light field to represent the scene geometry and appearance respectively. The SDF is directly supervised by geometry from stereo matching, and is refined […]

Growing up as an avid Call of Duty player, I was always curious about what factors led to a team winning or losing a match. Was it strictly based on the number of kills each player obtained? Was it who played the objective more? Or was it something different? Finally, after years of waiting, I decided that it was time to find my answers. Coupling my love for Call of Duty and my passion for data science, I began to […]