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Light Up the Shadows: Enhance Long-Tailed Entity Grounding with Concept-Guided Vision-Language Models

Our paper is accepted by ACL 2024 (Findings). 🎉

Overview

COG: a two-stage framework with COncept-Guided vision-language models for long-tailed entity grounding.

Multi-Modal Knowledge Graphs (MMKGs) are valuable for various tasks but are hard to scale due to the presence of mismatched images, especially for long-tailed entities with scarce images online. Addressing this, the method of using vision-language models enhanced with COncept Guidance is introduced, featuring a two-stage framework named COG. COG consists of a CONCEPT INTEGRATION module to accurately identify image-text pairs for long-tailed entities and an EVIDENCE FUSION module for explainability.


Overview of COG.

More details are in the Paper.

Setup Environment

Create a conda environment and install dependency:

conda create -n COG python=3.11
conda activate COG
pip install -r requirements.txt

🚀 Quick Start

classification.sh: training and evaluation scripts for classification tasks

# Training

# baseline: use only entity name
python3 main.py \
    --dataset data_bundle_new.pkl \
    --ptm clip \
    --batch_size 128 \
    --gpu 0 \
    --save_path ./ckps/ \
    --con_loss \
    --do_train \
    --method onlyent \
    --threshold 0.5 \
    --task classification \
    --epoch 10 

# COG with Concept Integration, using all concepts by default
python3 main.py \
    --dataset data_bundle_new.pkl \
    --ptm clip \
    --gpu 0 \
    --save_path ./ckps/ \
    --con_loss \
    --do_train \
    --method CI \
    --con_type all \
    --threshold 0.5 \
    --task classification \
    --epoch 10


# COG with Concept Integration and Evidence Fusion, using all concepts by default
python3 main.py \
    --dataset data_bundle_new.pkl \
    --ptm clip \
    --gpu 0 \
    --save_path ./ckps/ \
    --con_loss \
    --do_train \
    --method CI_EF \
    --con_type all \
    --threshold 0.5 \
    --task classification \
    --epoch 10


# Ablation: COG with Concept Integration, using blc concepts
python3 main.py \
    --dataset data_bundle_new.pkl \
    --ptm clip \
    --gpu 0 \
    --save_path ./ckps/ \
    --con_loss \
    --do_train \
    --method CI \
    --con_type blc \
    --threshold 0.5 \
    --task classification \
    --epoch 10


# Evaluation

# baseline: use only entity name
python3 main.py \
    --dataset data_bundle_new.pkl \
    --ptm clip \
    --gpu 0 \
    --load_path "" \
    --do_eval \
    --method onlyent \
    --threshold 0.5 \
    --task classification 


# COG with Concept Integration, using all concepts by default
python3 main.py \
    --dataset data_bundle_new.pkl \
    --ptm clip \
    --gpu 0 \
    --load_path "" \
    --do_eval \
    --method CI \
    --con_type all \
    --threshold 0.5 \
    --task classification 



# COG with Concept Integration and Evidence Fusion, using all concepts by default
python3 main.py \
    --dataset data_bundle_new.pkl \
    --ptm clip \
    --gpu 1 \
    --load_path "" \
    --do_eval \
    --method CI_EF \
    --con_type all \
    --threshold 0.5 \
    --task classification \
    --save_evidence


# Ablation: COG with Concept Integration, using blc concepts
python3 main.py \
    --dataset data_bundle_new.pkl \
    --ptm clip \
    --gpu 0 \
    --load_path "" \
    --do_eval \
    --method CI \
    --con_type blc \
    --threshold 0.5 \
    --task classification

ranking.sh: training and evaluation scripts for ranking tasks


# Training

# baseline: use only entity name
python3 main.py \
    --dataset data_bundle_new_lp.pkl \
    --ptm clip \
    --gpu 1 \
    --save_path ./ckps/ \
    --batch_size 16 \
    --do_train \
    --method onlyent \
    --task ranking


# COG with Concept Integration, using all concepts by default
python3 main.py \
    --dataset data_bundle_new_lp.pkl \
    --ptm clip \
    --gpu 1 \
    --con_type all  \
    --con_loss \
    --save_path ./ckps/ \
    --batch_size 16 \
    --do_train \
    --method CI \
    --task ranking


# Evaluation

# baseline: use only entity name
python3 main.py \
    --dataset data_bundle_new_lp.pkl \
    --ptm clip \
    --gpu 1 \
    --load_path "" \
    --batch_size 16 \
    --do_eval \
    --method onlyent \
    --task ranking \


# COG with Concept Integration, using all concepts by default
python3 main.py \
    --dataset data_bundle_new_lp.pkl \
    --ptm clip \
    --gpu 1 \
    --load_path "" \
    --batch_size 16 \
    --do_eval \
    --con_type all \
    --method CI \
    --task ranking \

Arguments

1. `--seed` refers to the random seed, with 42 as the default value.
2. `--dataset` refers to the dataset. Use data_bundle_new.pkl for classification; for ranking, use data_bundle_new_lp.pkl.
3. `--ptm` refers to the pre-trained vision-language model to be used.
4. `--ptm_lr` refers to the learning rate of the pre-trained vision-language model.
5. `--model_lr` refers to the learning rate of other modules (e.g. MLP layers).
6. `--batch_size` refers to the batch size.
7. `--epoch` refers to the number of training epochs.
8. `--weight_decay` refers to the weight decay rate.
9. `--load_path` refers to the path of the checkpoint for evaluation.
10. `--load_epoch` refers to the epoch for resuming training.
11. `--do_train` enables training mode.
12. `--do_eval` enables evaluation mode.
13. `--wandb` refers to using wandb for recording experimental results.
14. `--gpu` refers to the GPU IDs to be used.
15. `--save_path` refers to the path for saving checkpoints.
16. `--con_loss` enables using concept loss
17. `--project` refers to the wandb project name.
18. `--method` refers to the method to be used, containing 'onlyent' (using only entity name), 'CI' (Concept Integration), and 'CI_EF' (Concept Integration and Evidence Fusion).
19. `--con_type` refers to the concept type to be used, containing 'blc' (Basic-Level Concepts) and 'all' (All Concepts).
20. `--task` refers to the task, containing 'classification' and 'ranking'.
21. `--save_evidence` enables saving evidence from Evidence Fusion.
22. `--threshold` refers to the threshold for deciding whether a pair of text and image matches.

Contact

If you have any problems, please contact Yikai Zhang.

Citation

If our paper or related resources prove valuable to your research, we kindly ask for citation.

@inproceedings{zhang-etal-2024-light,
    title = "Light Up the Shadows: Enhance Long-Tailed Entity Grounding with Concept-Guided Vision-Language Models",
    author = "Zhang, Yikai  and
      He, Qianyu  and
      Wang, Xintao  and
      Yuan, Siyu  and
      Liang, Jiaqing  and
      Xiao, Yanghua",
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand and virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.findings-acl.793",
    pages = "13379--13389",
    abstract = "Multi-Modal Knowledge Graphs (MMKGs) have proven valuable for various downstream tasks. However, scaling them up is challenging because building large-scale MMKGs often introduces mismatched images (i.e., noise). Most entities in KGs belong to the long tail, meaning there are few images of them available online. This scarcity makes it difficult to determine whether a found image matches the entity. To address this, we draw on the Triangle of Reference Theory and suggest enhancing vision-language models with concept guidance. Specifically, we introduce COG, a two-stage framework with COncept-Guided vision-language models. The framework comprises a Concept Integration module, which effectively identifies image-text pairs of long-tailed entities, and an Evidence Fusion module, which offers explainability and enables human verification. To demonstrate the effectiveness of COG, we create a dataset of 25k image-text pairs of long-tailed entities. Our comprehensive experiments show that COG not only improves the accuracy of recognizing long-tailed image-text pairs compared to baselines but also offers flexibility and explainability.",
}