PyTorch

UMS for Multi-turn Response Selection in PyTorch

UMS for Multi-turn Response Selection PyTorch Implementation for AAAI’21 “Do Response Selection Models Really Know What’s Next? Utterance Manipulation Strategies for Multi-turn Response Selection” Implements the model described in the following paper Do Response Selection Models Really Know What’s Next? Utterance Manipulation Strategies for Multi-turn Response Selection. @inproceedings{whang2021ums, title={Do Response Selection Models Really Know What’s Next? Utterance Manipulation Strategies for Multi-turn Response Selection}, author={Whang, Taesun and Lee, Dongyub and Oh, Dongsuk and Lee, Chanhee and Han, Kijong and Lee, Dong-hun […]

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Field

Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds

This repository contains the PyTorch implementation for paper “PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds” (CVPR 2021)[arXiv] Installation Prerequisites Python 3.8 PyTorch 1.8 torch-scatter CUDA 10.2 RTX 2080 Ti tqdm, tensorboard, scipy, imageio, png conda create -n pvraft python=3.8 conda activate pvraft conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch conda install tqdm tensorboard scipy imageio pip install pypng pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.8.0+cu102.html Usage Data Preparation We follow HPLFlowNet to prepare FlyingThings3D and KITTI datasets. […]

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Text

Evaluating the Factual Consistency of Abstractive Text Summarization

factCC Evaluating the Factual Consistency of Abstractive Text SummarizationAuthors: Wojciech Kryściński, Bryan McCann, Caiming Xiong, and Richard Socher Introduction Currently used metrics for assessing summarization algorithms do not account for whether summaries are factually consistent with source documents.We propose a weakly-supervised, model-based approach for verifying factual consistency and identifying conflicts between source documents and a generated summary.Training data is generated by applying a series of rule-based transformations to the sentences of source documents.The factual consistency model is then trained jointly […]

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Task

Multi-Task Framework for Cross-Lingual Abstractive Summarization

MCLAS Multi-Task Framework for Cross-Lingual Abstractive Summarization (MCLAS)The code for ACL2021 paper Cross-Lingual Abstractive Summarization with Limited Parallel Resources (Paper). Some codes are borrowed from PreSumm (https://github.com/nlpyang/PreSumm). Environments Python version: This code is in Python3.7 Package Requirements: torch==1.1.0, transformers, tensorboardX, multiprocess, pyrouge Needs few changes to be compatible with torch 1.4.0~1.8.0, mainly tensor type (bool) bugs. Data Preparation To improve training efficiency, we preprocessed concatenated dataset (with target “monolingual summary + [LSEP] + cross-lingual summary”) and normal dataset (with target […]

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Images

A deeply supervised image fusion network for change detection

A-deeply-supervised-image-fusion-network-for-change-detection-in-remote-sensing-images A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images深度监督影像融合网络DSIFN用于高分辨率双时相遥感影像变化检测Official implement of the Paper：A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images. If you find this work helps in your research, please consider citing: 论文《A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sening images》的官方模型代码。如果该代码对你的研究有所帮助，烦请引用： Zhang, C., Yue, P., Tapete, D., Jiang, L., Shangguan, B., Huang, L., & Liu, G. (2020). […]

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Transformer

Feedback Transformer and Expire-Span with python

This repo contains the code for two papers: Feedback Transformer Expire-Span The training code is structured for long sequential modeling with Transformer-like architectures. Requirements You will need a CUDA-enabled GPU to run the code. Setup Run the following: pip install -r requirements.txt Feedback Transformer Introduced in Addressing Some Limitations of Transformers with Feedback Memory. Running Experiments from the Paper enwik8 Model Params Valid Test Feedback Transformer 77M 0.984 0.962 Numbers are Bits-Per-Character bash experiments/feedback/enwik8.sh Algorithmic Model 3 Variable 5 Variable […]

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Natural Language Processing

Multilingual Unsupervised Sentence Simplification by Mining Paraphrases

Multilingual Unsupervised Sentence Simplification Code and pretrained models to reproduce experiments in “MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases”. Prerequisites Linux with python 3.6 or above (not compatible with python 3.9 yet). Installing git clone [email protected]:facebookresearch/muss.git cd muss/ pip install -e . # Install package python -m spacy download en_core_web_md fr_core_news_md es_core_news_md # Install required spacy models How to use Some scripts might still contain a few bugs, if you notice anything wrong, feel free to open an issue […]

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PyTorch

Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning

radar-to-lidar-place-recognition This page is the implementation of a pre-print, implemented by PyTorch. If you have some questions on this project, please feel free to contact Huan Yin [email protected] . Method Data The files in matlab/RobotCar_data and matlab/MulRan_data can help you generate scancontext of radar and lidar submaps. Also, the generation of lidar submaps is included. Training The train_disco_lidar_quad.py is used for training lidar-to-lidar DiSCO. The train_disco_radar_quad.py is used for training radar-to-radar DiSCO. The train_joint_radar_lidar.py is used for training L2L, R2R […]

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Field

Editing Conditional Radiance Fields With Python

Editing Conditional Radiance Fields Editing Conditional Radiance FieldsSteven Liu, Xiuming Zhang, Zhoutong Zhang, Richard Zhang, Jun-Yan Zhu, Bryan RussellMIT, Adobe Research, CMUin arXiv:2105.06466, 2021. Editing Results Color Editing Our method propagates sparse 2D user scribbles to fill an object region, rendering the edit consistently across views. The user provides a color, a foreground scribble for the region to change, and a background scribble for regions to keep unchanged. To conduct the edit, we optimize a reconstruction-based loss to encourage the […]

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