Deep Learning, NLP, Project, Python, Unstructured Data

Sentiment Analysis Using Bidirectional Stacked LSTM

This article was published as a part of the Data Science Blogathon Sentiment Analysis Sentiment Analysis is the process of finding the sentiments of the text data. Sentiment Analysis falls under the text classification in Natural Language Processing. Sentiment Analysis would help us to know our customer reviews better. A sentiment denotes any one of the following, Positive, Negative, and Neutral. When we analyze the negative reviews of our products we can easily use those reviews to surmount the problems […]

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Natural Language Processing

Dice Loss for NLP Tasks with python

Dice Loss for NLP Tasks This repository contains code for Dice Loss for Data-imbalanced NLP Tasks at ACL2020. Setup Install Package Dependencies The code was tested in Python 3.6.9+ and Pytorch 1.7.1. If you are working on ubuntu GPU machine with CUDA 10.1, please run the following command to setup environment. $ virtualenv -p /usr/bin/python3.6 venv $ source venv/bin/activate $ pip install torch==1.7.1+cu101 torchvision==0.8.2+cu101 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html $ pip install -r requirements.txt Download BERT Model Checkpoints Before running the repo […]

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Video

Temporally-Sensitive Pretraining of Video Encoders for Localization Tasks

TSP TSP: Temporally-Sensitive Pretraining of Video Encoders for Localization Tasks This repository holds the source code, pretrained models, and pre-extracted features for the TSP method. Please cite this work if you find TSP useful for your research. @article{alwassel2020tsp, title={TSP: Temporally-Sensitive Pretraining of Video Encoders for Localization Tasks}, author={Alwassel, Humam and Giancola, Silvio and Ghanem, Bernard}, journal={arXiv preprint arXiv:2011.11479}, year={2020} } We provide pre-extracted features for ActivityNet v1.3 and THUMOS14 videos. The feature files are saved in H5 format, where we […]

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Color

Automatic color transfer across images in python

color-matcher color-matcher enables color transfer across images which comes in handy for automatic color-grading of photographs, paintings and film sequences as well as light-field and stopmotion corrections. The methods behind the mappings are based on the approach from Reinhard et al., the Monge-Kantorovich Linearization (MKL) as proposed by Pitie et al. and our analytical solution to a Multi-Variate Gaussian Distribution (MVGD) transfer in conjunction with classical histogram matching. As shown below our HM-MVGD-HM compound outperforms existing methods. Installation via pip: […]

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3D

Robust Shape Representation for Textured 3D Human Body Reconstruction

PeeledHuman [3DV 2020] PeeledHuman: Robust Shape Representation for Textured 3D Human Body Reconstruction AbstractWe introduce PeeledHuman – a novel shape representation of the human body that is robust to self-occlusions. PeeledHuman encodes the human body as a set of Peeled Depth and RGB maps in 2D, obtained by performing raytracing on the 3D body model and extending each ray beyond its first intersection. This formulation allows us to handle self-occlusions efficiently compared to other representations. Given a monocular RGB image, […]

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Simulation

Learning What To Do by Simulating the Past

Learning What To Do by Simulating the Past This repository contains code that implements the Deep Reward Learning by Simulating the Past (Deep RSLP) algorithm introduced in the paper “Learning What To Do by Simulating the Past”. This code is provided as is, and will not be maintained. Here we describe how to reproduce the experimental results reported in the paper. You can find video of policies trained with Deep RLSP here. Citation David Lindner, Rohin Shah, Pieter Abbeel, Anca […]

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Segmentation

Towards High-Quality Instance Segmentation with Fine-Grained Features

Towards High-Quality Instance Segmentation with Fine-Grained Features This repo is the official implementation of RefineMask: Towards High-Quality Instance Segmentation with Fine-Grained Features. Main Results Results on COCO Method Backbone Schedule AP AP* Checkpoint Mask R-CNN R50-FPN 1x 34.7 36.8 RefineMask R50-FPN 1x 37.3 40.6 download Mask R-CNN R50-FPN 2x 35.4 37.7 RefineMask R50-FPN 2x 37.8 41.2 download Mask R-CNN R101-FPN 1x 36.1 38.4 RefineMask R101-FPN 1x 38.6 41.8 download Mask R-CNN R101-FPN 2x 36.6 39.3 RefineMask R101-FPN 2x 39.0 42.4 […]

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3D

Back-tracing Representative Points for Voting-based 3D Object Detection in Point Clouds

BRNet Back-tracing Representative Points for Voting-based 3D Object Detection in Point Clouds. Introduction This is a release of the code of our paper Back-tracing Representative Points for Voting-based 3D Object Detection in Point Clouds, CVPR 2021. Authors: Bowen Cheng, Lu Sheng*, Shaoshuai Shi, Ming Yang, Dong Xu (*corresponding author) [arxiv] In this repository, we reimplement BRNet based on mmdetection3d for easier usage. Citation If you find our work useful in your research, please consider citing: @inproceedings{cheng2021brnet, title={Back-tracing Representative Points for […]

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Models

SMPLicit: Topology-aware Generative Model for Clothed People

SMPLicit Implementation for the paper SMPLicit: Topology-aware Generative Model for Clothed People (CVPR 2021) Installation Follow these commands to install SMPLicit in your environment. The required libraries are standard, with the possible exception of Kaolin which requires a particular version to run with the current code. git clonehttps://github.com/enriccorona/SMPLicit cd SMPLicit Install the dependencies listed in requirements.txt: pip install -r requirements.txt In particular, we use Kaolin v0.1 (see installation command) which should be easy to install. However, if you want to […]

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Dataset

Datasets from Instructions In Python

Datasets from Instructions This repository contains the code for Generating Datasets with Pretrained Language Models. The paper introduces a method called Datasets from Instructions (DINO sauropod) that enables pretrained language models to generate entire datasets from scratch. 🔧 Setup All requirements for DINO can be found in requirements.txt. You can install all required packages in a new environment with pip install -r requirements.txt. 💬 CLI Usage Single Texts To generate datasets for (single) text classification, you can use DINO as […]

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