Code accompanying the paper Say As You Wish: Fine-grained Control of Image Caption Generation with Abstract Scene Graphs (Chen et al., CVPR 2020, Oral).

Related tags

Deep Learningasg2cap
Overview

Say As You Wish: Fine-grained Control of Image Caption Generation with Abstract Scene Graphs

This repository contains PyTorch implementation of our paper Say As You Wish: Fine-grained Control of Image Caption Generation with Abstract Scene Graphs (CVPR 2020).

Overview of ASG2Caption Model

Prerequisites

Python 3 and PyTorch 1.3.

# clone the repository
git clone https://github.com/cshizhe/asg2cap.git
cd asg2cap
# clone caption evaluation codes
git clone https://github.com/cshizhe/eval_cap.git
export PYTHONPATH=$(pwd):${PYTHONPATH}

Training & Inference

cd controlimcap/driver

# support caption models: [node, node.role, 
# rgcn, rgcn.flow, rgcn.memory, rgcn.flow.memory]
# see our paper for details
mtype=rgcn.flow.memory 

# setup config files
# you should modify data paths in configs/prepare_*_imgsg_config.py
python configs/prepare_coco_imgsg_config.py $mtype
resdir='' # copy the output string of the previous step

# training
python asg2caption.py $resdir/model.json $resdir/path.json $mtype --eval_loss --is_train --num_workers 8

# inference
python asg2caption.py $resdir/model.json $resdir/path.json $mtype --eval_set tst --num_workers 8

Datasets

Annotations

Annotations for MSCOCO and VisualGenome datasets can be download from GoogleDrive.

  • (Image, ASG, Caption) annotations: regionfiles/image_id.json
JSON Format:
{
	"region_id": {
		"objects":[
			{
	     		"object_id": int, 
	     		"name": str, 
	     		"attributes": [str],
				"x": int,
				"y": int, 
				"w": int, 
				"h": int
			}],
  	  "relationships": [
			{
				"relationship_id": int,
				"subject_id": int,
				"object_id": int,
				"name": str
			}],
  	  "phrase": str,
  }
}

  • vocabularies int2word.npy: [word] word2int.json: {word: int}

  • data splits: public_split directory trn_names.npy, val_names.npy, tst_names.npy

Features

Features for MSCOCO and VisualGenome datasets are available at BaiduNetdisk (code: 6q32).

We also provide pretrained models and codes to extract features for new images.

format: npy array, shape=(num_fts, dim_ft) corresponding to the order in data_split names

format: hdf5 files, "image_id".jpg.hdf5

key: 'image_id'.jpg

attrs: {"image_w": int, "image_h": int, "boxes": 4d array (x1, y1, x2, y2)}

Result Visualization

Examples

Citations

If you use this code as part of any published research, we'd really appreciate it if you could cite the following paper:

@article{chen2020say,
  title={Say As You Wish: Fine-grained Control of Image Caption Generation with Abstract Scene Graphs},
  author={Chen, Shizhe and Jin, Qin and Wang, Peng and Wu, Qi},
  journal={CVPR},
  year={2020}
}

License

MIT License

Owner
Shizhe Chen
Shizhe Chen
GitHub Repository
A basic implementation of Layer-wise Relevance Propagation (LRP) in PyTorch.

Layer-wise Relevance Propagation (LRP) in PyTorch Basic unsupervised implementation of Layer-wise Relevance Propagation (Bach et al., Montavon et al.)

Kai Fabi 28 Dec 26, 2022
PyTorch Implementation of CycleGAN and SSGAN for Domain Transfer (Minimal)

MNIST-to-SVHN and SVHN-to-MNIST PyTorch Implementation of CycleGAN and Semi-Supervised GAN for Domain Transfer. Prerequites Python 3.5 PyTorch 0.1.12

Yunjey Choi 401 Dec 30, 2022
CVNets: A library for training computer vision networks

CVNets: A library for training computer vision networks This repository contains the source code for training computer vision models. Specifically, it

Apple 1.1k Jan 03, 2023
Jittor implementation of Recursive-NeRF: An Efficient and Dynamically Growing NeRF

Recursive-NeRF: An Efficient and Dynamically Growing NeRF This is a Jittor implementation of Recursive-NeRF: An Efficient and Dynamically Growing NeRF

33 Nov 30, 2022
Create and implement a deep learning library from scratch.

In this project, we create and implement a deep learning library from scratch. Table of Contents Deep Leaning Library Table of Contents About The Proj

Rishabh Bali 22 Aug 23, 2022
LSTM model trained on a small dataset of 3000 names written in PyTorch

LSTM model trained on a small dataset of 3000 names. Model generates names from model by selecting one out of top 3 letters suggested by model at a time until an EOS (End Of Sentence) character is no

Sahil Lamba 1 Dec 20, 2021
Sparse Physics-based and Interpretable Neural Networks

Sparse Physics-based and Interpretable Neural Networks for PDEs This repository contains the code and manuscript for research done on Sparse Physics-b

28 Jan 03, 2023
K-Means Clustering and Hierarchical Clustering Unsupervised Learning Solution in Python3.

Unsupervised Learning - K-Means Clustering and Hierarchical Clustering - The Heritage Foundation's Economic Freedom Index Analysis 2019 - By David Sal

David Salako 1 Jan 12, 2022
OOD Dataset Curator and Benchmark for AI-aided Drug Discovery

🔥 DrugOOD 🔥 : OOD Dataset Curator and Benchmark for AI Aided Drug Discovery This is the official implementation of the DrugOOD project, this is the

108 Dec 17, 2022
Image Segmentation with U-Net Algorithm on Carvana Dataset using AWS Sagemaker

Image Segmentation with U-Net Algorithm on Carvana Dataset using AWS Sagemaker This is a full project of image segmentation using the model built with

Htin Aung Lu 1 Jan 04, 2022
PyTorch implementation of 'Gen-LaneNet: a generalized and scalable approach for 3D lane detection'

(pytorch) Gen-LaneNet: a generalized and scalable approach for 3D lane detection Introduction This is a pytorch implementation of Gen-LaneNet, which p

Yuliang Guo 233 Jan 06, 2023
A modular PyTorch library for optical flow estimation using neural networks

A modular PyTorch library for optical flow estimation using neural networks

neu-vig 113 Dec 20, 2022
这是一个deeplabv3-plus-pytorch的源码，可以用于训练自己的模型。

DeepLabv3+：Encoder-Decoder with Atrous Separable Convolution语义分割模型在Pytorch当中的实现 目录 性能情况 Performance 所需环境 Environment 注意事项 Attention 文件下载 Download 训练步骤

Bubbliiiing 350 Dec 28, 2022
Baseline and template code for node21 detection track

Nodule Detection Algorithm This codebase implements a baseline model, Faster R-CNN, for the nodule detection track in NODE21. It contains all necessar

node21challenge 11 Jan 15, 2022
SelfRemaster: SSL Speech Restoration

SelfRemaster: Self-Supervised Speech Restoration Official implementation of SelfRemaster: Self-Supervised Speech Restoration with Analysis-by-Synthesi

Takaaki Saeki 46 Jan 07, 2023
SpeechNAS Better Trade off between Latency and Accuracy for Large Scale Speaker Verification

SpeechNAS Better Trade off between Latency and Accuracy for Large Scale Speaker Verification

Wentao Zhu 24 May 20, 2022
Official code for "Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer. ICCV2021".

Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer. ICCV2021. Introduction We proposed a novel model training paradi

Lucas 103 Dec 14, 2022
Generative Models for Graph-Based Protein Design

Graph-Based Protein Design This repo contains code for Generative Models for Graph-Based Protein Design by John Ingraham, Vikas Garg, Regina Barzilay

John Ingraham 159 Dec 15, 2022
Implementation of gMLP, an all-MLP replacement for Transformers, in Pytorch

Implementation of gMLP, an all-MLP replacement for Transformers, in Pytorch

Phil Wang 383 Jan 02, 2023
Official repository of Semantic Image Matting

Semantic Image Matting This is the official repository of Semantic Image Matting (CVPR2021). Overview Natural image matting separates the foreground f

192 Dec 29, 2022