Scene Graph Modification Based on Natural Language Commands

Descriptions

This repo contains source code and pre-processed corpora for "Scene Graph Modification Based on Natural Language Commands" (accepted to Findings of EMNLP 2020) (paper)

Demo

We demonstrate four different operations one can execute on scene graphs:

Dependencies

python3
pytorch==1.1
networkx
spacy>=2.3.1

Usage

git clone https://github.com/xlhex/SceneGraphModification.git

Data

General Information

We create three different datasets for our scene graph modification task: 1) MSCOCO data, 2) GCC data and 3) crowdsourced data. The first two are constructed with some heuristic approaches, while the last one is crowdsourced from Amazon Mechanical Turk (please refer to our paper for the details).

Each dataset is partitioned into train/dev/test, with each split consisting of the following files:

source scene graph: {split}_src_graph.bin
modification query: {split}_src_text.txt
target scene graph: {split}_tgt_graph.bin

The datasets can be downloaded from here

Train a model

The following code shows how we can train an early fusion (cross-attention) model for a given dataset

cd code

DATA=PATH_TO_YOUR_DATA
CKPT_DIR=
EPOCH=20
FUSION=early

log="${CKPT_DIR}/log.txt"
if [ ! -d $CKPT_DIR ];then
    mkdir -p $CKPT_DIR
fi

# build a dictionary for training and inference
python preprocess.py $DATA

python train.py --data-dir $DATA --epochs $EPOCH --seed 1 --ckpt-dir $CKPT_DIR --modification $FUSION --batch-size 256 --accumulation-steps 1 > $log

Inference

The following code shows how we generate a target graph, given the source graph and a modification query

cd code

DATA=PATH_TO_YOUR_DATA
CKPT_DIR=
FUSION=early

python search.py --data-dir $DATA --greedy-search --batch-size 1 --ckpt-dir $CKPT_DIR --modification $FUSION

Instance Visualisation

You can visualise some modification instances. For example, the following code will visualise the first two instances. The rendered source graphs and target graphs can be found at: scripts/display

cd scripts
SRC_GRAPH=PATH_TO_SRC_GRAPH
TGT_GRAPH=PATH_TO_TGT_GRAPH
QUERY=PATH_TO_QUERY

python visualisation.py --src-graph $SRC_GRAPH --tgt-graph $TGT_GRAPH --graph-idx 0,1 --query $QUERY

Citation

Please cite as:

@misc{he2020scene,
      title={Scene Graph Modification Based on Natural Language Commands}, 
      author={Xuanli He and Quan Hung Tran and Gholamreza Haffari and Walter Chang and Trung Bui and Zhe Lin and Franck Dernoncourt and Nhan Dam},
      year={2020},
      eprint={2010.02591},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

mingzi151/SceneGraphModification