PyTorch Implementation of our paper Explain Me the Painting: Multi-Topic Knowledgeable Art Description Generation

Overview

Explain Me the Painting: Multi-Topic Knowledgeable Art Description Generation

[Code] [Data] [Project Page]

Official PyTorch Implementation of our paper Explain Me the Painting: Multi-Topic Knowledgeable Art Description Generation, published at ICCV 2021.

Have you ever looked at a painting and wondered what is the story behind it? This work presents a framework to bring art closer to people by generating comprehensive descriptions of fine-art paintings. Generating informative descriptions for artworks, however, is extremely challenging, as it requires to 1) describe multiple aspects of the image such as its style, content, or composition, and 2) provide background and contextual knowledge about the artist, their influences, or the historical period. To address these challenges, we introduce a multi-topic and knowledgeable art description framework, which modules the generated sentences according to three artistic topics and, additionally, enhances each description with external knowledge. The framework is validated through an exhaustive analysis, both quantitative and qualitative, as well as a comparative human evaluation, demonstrating outstanding results in terms of both topic diversity and information veracity.

Setup

Requirements

The code are tested under Python3.6 with the following packages:

torch==1.1.0
torchvision==0.2.2
numpy==1.16.2
visdom==0.1.8.9
transformers==2.1.1
nltk==3.2.3
stanfordcorenlp==3.9.1.1
scipy==1.3.1
pandas==0.25.1

Prepare Data

1.Download the dataset from this repository

2.Put the annotation folder into the MaskedSentenceGeneration

Masked Sentence Generation

cd MaskedSentenceGeneration
python prepare_dataset.py
bash train.sh
bash test_one.sh / bash test_all.sh

Knowledge Retrieval

Please look into here

Knowledge Filling

cd KnowledgeFilling
python create_dataset_drqa_src.py
bash train.sh
bash test.sh

Citation

If you find the data in this repository useful, please cite our paper:

@InProceedings{bai2021explain,
   author    = {Zechen Bai and Yuta Nakashima and Noa Garcia},
   title     = {Explain Me the Painting: Multi-Topic Knowledgeable Art Description Generation},
   booktitle = {International Conference in Computer Vision},
   year      = {2021},
}
Owner
Zechen Bai
No one designed us, we are just bad codes.
Zechen Bai
Planning from Pixels in Environments with Combinatorially Hard Search Spaces -- NeurIPS 2021

PPGS: Planning from Pixels in Environments with Combinatorially Hard Search Spaces Environment Setup We recommend pipenv for creating and managing vir

Autonomous Learning Group 11 Jun 26, 2022
Dahua Camera and Doorbell Home Assistant Integration

Home Assistant Dahua Integration The Dahua Home Assistant integration allows you to integrate your Dahua cameras and doorbells in Home Assistant. It's

Ronnie 216 Dec 26, 2022
SE-MSCNN: A Lightweight Multi-scaled Fusion Network for Sleep Apnea Detection Using Single-Lead ECG Signals

SE-MSCNN: A Lightweight Multi-scaled Fusion Network for Sleep Apnea Detection Using Single-Lead ECG Signals Abstract Sleep apnea (SA) is a common slee

9 Dec 21, 2022
Pytorch implementation of four neural network based domain adaptation techniques: DeepCORAL, DDC, CDAN and CDAN+E. Evaluated on benchmark dataset Office31.

Deep-Unsupervised-Domain-Adaptation Pytorch implementation of four neural network based domain adaptation techniques: DeepCORAL, DDC, CDAN and CDAN+E.

Alan Grijalva 49 Dec 20, 2022
Evaluation toolkit of the informative tracking benchmark comprising 9 scenarios, 180 diverse videos, and new challenges.

Informative-tracking-benchmark Informative tracking benchmark (ITB) higher diversity. It contains 9 representative scenarios and 180 diverse videos. m

Xin Li 15 Nov 26, 2022
Pytorch Implementation of "Contrastive Representation Learning for Exemplar-Guided Paraphrase Generation"

CRL_EGPG Pytorch Implementation of Contrastive Representation Learning for Exemplar-Guided Paraphrase Generation We use contrastive loss implemented b

YHR 25 Nov 14, 2022
Machine Learning toolbox for Humans

Reproducible Experiment Platform (REP) REP is ipython-based environment for conducting data-driven research in a consistent and reproducible way. Main

Yandex 662 Nov 20, 2022
[CVPR 2021] VirTex: Learning Visual Representations from Textual Annotations

VirTex: Learning Visual Representations from Textual Annotations Karan Desai and Justin Johnson University of Michigan CVPR 2021 arxiv.org/abs/2006.06

Karan Desai 533 Dec 24, 2022
Source code of the paper PatchGraph: In-hand tactile tracking with learned surface normals.

PatchGraph This repository contains the source code of the paper PatchGraph: In-hand tactile tracking with learned surface normals. Installation Creat

Paloma Sodhi 11 Dec 15, 2022
Accurate Phylogenetic Inference with Symmetry-Preserving Neural Networks

Accurate Phylogenetic Inference with a Symmetry-preserving Neural Network Model Claudia Solis-Lemus Shengwen Yang Leonardo Zepeda-Núñez This repositor

Leonardo Zepeda-Núñez 2 Feb 11, 2022
Open Source Light Field Toolbox for Super-Resolution

BasicLFSR BasicLFSR is an open-source and easy-to-use Light Field (LF) image Super-Ressolution (SR) toolbox based on PyTorch, including a collection o

Squidward 50 Nov 18, 2022
From the basics to slightly more interesting applications of Tensorflow

TensorFlow Tutorials You can find python source code under the python directory, and associated notebooks under notebooks. Source code Description 1 b

Parag K Mital 5.6k Jan 09, 2023
The code used for the free [email protected] Webinar series on Reinforcement Learning in Finance

Reinforcement Learning in Finance [email protected] Webinar This repository provides the code f

Yves Hilpisch 62 Dec 22, 2022
The repository is for safe reinforcement learning baselines.

Safe-Reinforcement-Learning-Baseline The repository is for Safe Reinforcement Learning (RL) research, in which we investigate various safe RL baseline

172 Dec 19, 2022
GRF: Learning a General Radiance Field for 3D Representation and Rendering

GRF: Learning a General Radiance Field for 3D Representation and Rendering [Paper] [Video] GRF: Learning a General Radiance Field for 3D Representatio

Alex Trevithick 243 Dec 29, 2022
Download and preprocess popular sequential recommendation datasets

Sequential Recommendation Datasets This repository collects some commonly used sequential recommendation datasets in recent research papers and provid

125 Dec 06, 2022
PyTorch implementation of our ICCV2021 paper: StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimation

StructDepth PyTorch implementation of our ICCV2021 paper: StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimat

SJTU-ViSYS 112 Nov 28, 2022
This provides the R code and data to replicate results in "The USS Trustee’s risky strategy"

USSBriefs2021 This provides the R code and data to replicate results in "The USS Trustee’s risky strategy" by Neil M Davies, Jackie Grant and Chin Yan

1 Oct 30, 2021
This's an implementation of deepmind Visual Interaction Networks paper using pytorch

Visual-Interaction-Networks An implementation of Deepmind visual interaction networks in Pytorch. Introduction For the purpose of understanding the ch

Mahmoud Gamal Salem 166 Dec 06, 2022
This repository contains implementations and illustrative code to accompany DeepMind publications

DeepMind Research This repository contains implementations and illustrative code to accompany DeepMind publications. Along with publishing papers to a

DeepMind 11.3k Dec 31, 2022