FactualSceneGraph 0.3.0

A package for scene graph parsing and evaluation

FACTUAL: A Benchmark for Faithful and Consistent Textual Scene Graph Parsing

Welcome to the official repository for the ACL 2023 paper:
FACTUAL: A Benchmark for Faithful and Consistent Textual Scene Graph Parsing. Here, you'll find both the code and dataset associated with our research.

Dataset

The FACTUAL Scene Graph dataset includes 40,369 instances with lemmatized predicates/relations.

FACTUAL Scene Graph dataset:

• Storage: data/factual_sg/factual_sg.csv
• From Huggingface: load_dataset('lizhuang144/FACTUAL_Scene_Graph')

Splits:

• Random Split:
  • Train: data/factual_sg/random/train.csv
  • Test: data/factual_sg/random/test.csv
  • Dev: data/factual_sg/random/dev.csv
• Length Split:
  • Train: data/factual_sg/length/train.csv
  • Test: data/factual_sg/length/test.csv
  • Dev: data/factual_sg/length/dev.csv

Data Fields:

• image_id: The ID of the image in Visual Genome.
• region_id: The ID of the region in Visual Genome.
• caption: The caption of the image region.
• scene_graph: The scene graph of the image region and caption.

Related Resources: Visual Genome

FACTUAL-MR dataset:

• TODO: Add cleaned FACTUAL-MR dataset.

VG Scene Graph dataset:

• From Huggingface: load_dataset('lizhuang144/VG_scene_graph_clean')
• Details: Cleaned to exclude empty instances; includes 2.9 million instances.

FACTUAL Scene Graph dataset with identifiers:

• From Huggingface: load_dataset('lizhuang144/FACTUAL_Scene_Graph_ID')
• Enhancements: Contains verb identifiers, passive voice indicators, and node indexes.

Scene Graph Parsing Models

Simplified Model Training Without Node Indexes and Passive Identifiers

The following table shows the performance comparison of various scene graph parsing models. Notably, the original SPICE parser performs worse than our more recent models.

Performance Metrics Explained:

• SPICE F-score: A metric that measures the similarity between candidate and reference scene graph representations derived from captions. It assesses the quality of scene graph parsing by evaluating how well the parser's output matches the ground truth graph in terms of propositional content.
• Exact Set Match: Adapted from the methodology described by Yu et al., 2019, this metric evaluates the parser's accuracy by verifying whether the strings of parsed facts match the ground truth facts, without considering the ordering of those facts. This adaptation is a stringent accuracy measure, necessitating an exact correspondence between the candidate and ground truth facts.

Note: It is important to note that in the original work of Yu et al., 2019, the metric was applied to SQL clauses, whereas in our context, it has been tailored to assess scene graph facts.

Model	Set Match	SPICE	Soft-SPICE	Model Weight
SPICE Parser	13.00	56.15		modified-SPICE-score
(pre) Flan-T5-large	81.63	93.20	98.75	flan-t5-large-VG-factual-sg
(pre) Flan-T5-base	80.77	92.97	98.87	flan-t5-base-VG-factual-sg
(pre) Flan-T5-small	78.18	92.26	98.67	flan-t5-small-VG-factual-sg

The prefix "(pre)" indicates models that were pre-trained on the VG scene graph dataset before being fine-tuned on the FACTUAL dataset. The outdated SPICE parser, despite its historical significance, shows a Set Match rate of only 13% and a SPICE score of 56.15, which is significantly lower than the more recent Flan-T5 models fine-tuned on FACTUAL data.

Note: In the training of these models, we have removed the node index, meaning that different nodes with identical names will not be distinguished by their indexes. Furthermore, passive identifiers, such as 'p:', are excluded, and verbs and prepositions have been merged. This format, while losing some information from the FACTUAL-MR dataset, remains compatible with the Visual Genome scene graphs and can be effectively used in downstream scene graph tasks.

Enhanced Scene Graph Parsing with Node Indexes and Verb Identifiers

Enhanced scene graph parsing includes detailed annotations such as verb identifiers and node indexes, which offer a more nuanced understanding of the relationships within the input text. For example:

• The sentence "A monkey is sitting next to another monkey" is parsed as: ( monkey, v:sit next to, monkey:1 ) Here, "v:" indicates a verb, and ":1" differentiates the second "monkey" as a unique entity.

• For "A car is parked on the ground", the scene graph is: ( car, pv:park on, ground ) The "pv:" prefix highlights "park" as a passive verb, underscoring the significance of node order in the graph.

This advanced parsing technique offers substantial enhancements over the original Visual Genome (VG) scene graphs by:

• Uniquely Identifying Similar Entities: Assigning indexes to nodes with the same name allows for clear differentiation between identical entities.
• Detailing Predicates: Annotating each predicate with the specific verb and its tense provides richer contextual information.

Such improvements are invaluable for complex downstream tasks, as they facilitate a deeper semantic understanding of the scenes.

Model Performance with Advanced Parsing:

Model	Set Match	SPICE	Soft-SPICE	Model Weight
(pre) Flan-T5-large	81.03	93.00	98.66	flan-t5-large-VG-factual-sg-id
(pre) Flan-T5-base	81.37	93.29	98.76	flan-t5-base-VG-factual-sg-id
(pre) Flan-T5-small	79.64	92.40	98.53	flan-t5-small-VG-factual-sg-id

The acronym (pre) stands for models that were pre-trained on VG and then fine-tuned on FACTUAL, indicating a two-phase learning process that enhances model performance.

Usage Example

This section demonstrates how to use our models for scene graph parsing. We provide two examples: a basic usage with our pre-trained model and a more advanced usage with the SceneGraphParser class.

Basic Usage

First, install the necessary package:

pip install FactualSceneGraph

Then, you can use our pre-trained model as follows:

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("lizhuang144/flan-t5-base-VG-factual-sg")
model = AutoModelForSeq2SeqLM.from_pretrained("lizhuang144/flan-t5-base-VG-factual-sg")

text = tokenizer(
    "Generate Scene Graph: 2 pigs are flying on the sky with 2 bags on their backs",
    max_length=200,
    return_tensors="pt",
    truncation=True
)

generated_ids = model.generate(
    text["input_ids"],
    attention_mask=text["attention_mask"],
    use_cache=True,
    decoder_start_token_id=tokenizer.pad_token_id,
    num_beams=1,
    max_length=200,
    early_stopping=True
)

print(tokenizer.decode(generated_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=True))
# Output: `( pigs , is , 2 ) , ( bags , on back of , pigs ), ( bags , is , 2 ) , ( pigs , fly on , sky )`

Note: In this example, the predicate 'is' is referred to as 'has_attribute'.

Advanced Usage with SceneGraphParser For a more advanced parsing, utilize the SceneGraphParser class:

from factual_scene_graph.parser.scene_graph_parser import SceneGraphParser

parser = SceneGraphParser('lizhuang144/flan-t5-base-VG-factual-sg', device='cpu')
text_graph = parser.parse(["2 beautiful pigs are flying on the sky with 2 bags on their backs"], beam_size=1, return_text=True)
graph_obj = parser.parse(["2 beautiful and strong pigs are flying on the sky with 2 bags on their backs"], beam_size=1, return_text=False,max_output_len=128)

print(text_graph[0])
# Output: ( pigs , is , 2 ) , ( pigs , is , beautiful ) , ( bags , on back of , pigs ) , ( pigs , fly on , sky ) , ( bags , is , 2 )

from sng_parser.utils import tprint
tprint(graph_obj[0])

This will produce a formatted scene graph output:

Entities:
+----------+------------+------------------+
| Entity   | Quantity   | Attributes       |
|----------+------------+------------------|
| pigs     | 2          | beautiful,strong |
| bags     | 2          |                  |
| sky      |            |                  |
+----------+------------+------------------+
Relations:
+-----------+------------+----------+
| Subject   | Relation   | Object   |
|-----------+------------+----------|
| pigs      | fly on     | sky      |
| bags      | on back of | pigs     |
+-----------+------------+----------+

Factual Scene Graph Evaluation

This package provides implementations for evaluating scene graphs using SPICE, SoftSPICE, and Set Match metrics. These evaluations can be performed on various inputs, including captions and scene graphs in both list and nested list formats.

Supported Input Formats

• (list of candidate_captions, list of list reference_captions)
• (list of candidate_captions, list of list reference_graphs)
• (list of candidate_graphs, list of list reference_graphs)

Installation

pip install FactualSceneGraph

Usage

Below are examples demonstrating how to use the evaluation methods provided in this package.

Example 1: Testing Scene Graph Parsing

This example demonstrates evaluating a single scene graph using the SPICE method.

import pandas as pd
import torch
from factual_scene_graph.evaluation.evaluator import Evaluator
from factual_scene_graph.parser.scene_graph_parser import SceneGraphParser

def test_scene_graph_parsing():
    device = "cuda" if torch.cuda.is_available() else "cpu"
    parser = SceneGraphParser('lizhuang144/flan-t5-base-VG-factual-sg', device=device)
    evaluator = Evaluator(parser=parser, device='cuda:0')

    scores = evaluator.evaluate(
        ["2 beautiful pigs are flying on the sky with 2 bags on their backs"],
        [['( pigs , is , beautiful ) , ( bags , on back of , pigs ) , ( bags , is , 2 ) , ( pigs , is , 2 ) , ( pigs , fly on , sky )']],
        method='spice',
        beam_size=1,
        max_output_len=128
    )
    print(scores)

# Uncomment to run the example
# test_scene_graph_parsing()

Example 2: Testing Scene Graph Parsing on the Test Set of FACTUAL Random Split

This example demonstrates evaluating a dataset of scene graphs using SPICE, Set Match, and SoftSPICE methods.

import pandas as pd
import torch
from factual_scene_graph.evaluation.evaluator import Evaluator
from factual_scene_graph.parser.scene_graph_parser import SceneGraphParser

def test_scene_graph_parsing_on_random():
    device = "cuda" if torch.cuda.is_available() else "cpu"
    parser = SceneGraphParser('lizhuang144/flan-t5-base-VG-factual-sg', device=device, lemmatize=False)
    evaluator = Evaluator(parser=parser, text_encoder_checkpoint='all-MiniLM-L6-v2', device='cuda:0', lemmatize=True)

    random_data_pd = pd.read_csv('data/factual_sg/random/test.csv')
    random_data_captions = random_data_pd['caption'].tolist()
    random_data_graphs = [[scene] for scene in random_data_pd['scene_graph'].tolist()]

    # Evaluating using SPICE
    spice_scores, cand_graphs, ref_graphs = evaluator.evaluate(
        random_data_captions, 
        random_data_graphs, 
        method='spice', 
        beam_size=1, 
        batch_size=128, 
        max_input_len=256, 
        max_output_len=256, 
        return_graphs=True
    )
    print('SPICE scores for random test set:', sum(spice_scores)/len(spice_scores))

    # Evaluating using Set Match
    set_match_scores = evaluator.evaluate(cand_graphs, ref_graphs, method='set_match', beam_size=1)
    print('Set Match scores for random test set:', sum(set_match_scores)/len(set_match_scores))

    # Evaluating using Soft-SPICE
    soft_spice_scores = evaluator.evaluate(cand_graphs, ref_graphs, method='soft_spice', beam_size=1)
    print('Soft-SPICE scores for random test set:', sum(soft_spice_scores)/len(soft_spice_scores))

# Uncomment to run the example
# test_scene_graph_parsing_on_random()

Human Correlation Performance on the Flickr8k Dataset

In our study, we evaluated the correlation of various metrics with human judgment on the Flickr8k dataset. This comparison helps in understanding how well each metric aligns with human perception.

Results

Below is a table showing the Tau-c correlation values for different models:

Model	Tau-c
SPICE(Official)	45.02
SPICE(Ours)	42.51
Soft-SPICE	54.00
RefCLIPScore	53.00
BERTScore	36.71

Notes on Implementations

• The default parser checkpoint we use is lizhuang144/flan-t5-base-VG-factual-sg.
• The official SPICE implementation can be found at Modified SPICE Score. Our implementation of SPICE, while slightly underperforming in comparison due to a less complex synonym-matching mechanism, offers ease of use.
• In our paper, we employ SPICE(Ours) for measuring parser performance in Table 3, as it does not influence the ranking of models. However, for direct comparison with previous studies in Tables 5 and 6, we use the official implementation of SPICE.

Replicating the Results

To replicate the human correlation results for Our SPICE and Soft-SPICE, please refer to the script located at tests/test_metric_human_correlation.py. This script provides a straightforward way to validate our findings.

Citation

To cite this work, please use the following Bibtex entry:

@inproceedings{li-etal-2023-factual,
    title = "{FACTUAL}: A Benchmark for Faithful and Consistent Textual Scene Graph Parsing",
    author = "Li, Zhuang  and
      Chai, Yuyang  and
      Zhuo, Terry Yue  and
      Qu, Lizhen  and
      Haffari, Gholamreza  and
      Li, Fei  and
      Ji, Donghong  and
      Tran, Quan Hung",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.398",
    pages = "6377--6390",
    abstract = "Textual scene graph parsing has become increasingly important in various vision-language applications, including image caption evaluation and image retrieval. However, existing scene graph parsers that convert image captions into scene graphs often suffer from two types of errors. First, the generated scene graphs fail to capture the true semantics of the captions or the corresponding images, resulting in a lack of faithfulness. Second, the generated scene graphs have high inconsistency, with the same semantics represented by different annotations.To address these challenges, we propose a novel dataset, which involves re-annotating the captions in Visual Genome (VG) using a new intermediate representation called FACTUAL-MR. FACTUAL-MR can be directly converted into faithful and consistent scene graph annotations. Our experimental results clearly demonstrate that the parser trained on our dataset outperforms existing approaches in terms of faithfulness and consistency. This improvement leads to a significant performance boost in both image caption evaluation and zero-shot image retrieval tasks. Furthermore, we introduce a novel metric for measuring scene graph similarity, which, when combined with the improved scene graph parser, achieves state-of-the-art (SOTA) results on multiple benchmark datasets for the aforementioned tasks.",
}

Acknowledgments

This project has been developed with the use of code from the SceneGraphParser repository by Jiayuan Mao. We gratefully acknowledge their pioneering work and contributions to the open-source community.