EVCap: Retrieval-Augmented Image Captioning with External Visual-Name Memory for Open-World Comprehension

Jiaxuan Li^*1, Minh-Duc Vo^*1, Akihiro Sugimoto², Hideki Nakayama¹

¹The University of Tokyo, ²National Institute of Informatics

^*equal contribution

Large language models (LLMs)-based image captioning has the capability of describing objects not explicitly observed in training data; yet novel objects occur frequently, necessitating the requirement of sustaining up-to-date object knowledge for open-world comprehension. Instead of relying on large amounts of data and scaling up network parameters, we introduce a highly effective retrieval-augmented image captioning method that prompts LLMs with object names retrieved from External Visual-name memory (EVCAP). We build ever-changing object knowledge memory using objects’ visuals and names, enabling us to (i) update the memory at a minimal cost and (ii) effortlessly augment LLMs with retrieved object names utilizing a lightweight and fast-to-train model. Our model, which was trained only on the COCO dataset, can be adapted to out-domain data without additional fine-tuning or retraining. Our comprehensive experiments conducted on various benchmarks and synthetic commonsense-violating data demonstrate that EVCAP, comprising solely 3.97M trainable parameters, exhibits superior performance compared to other methods of equivalent model size scale. Notably, it achieves competitive performance against specialist SOTAs with an enormous number of parameters.

Setup

Install the required packages using conda with the provided environment.yaml file.

Training

Train EVCap on the COCO training dataset, using the scripts/train_evcap.sh script.

Evaluation

Evaluate the trained EVCap on the COCO test set, NoCaps validation set, and Flickr30k test set, using the following script (bash scripts/eval_evcap_*.sh eval_evcap_* n), respectively:

bash scripts/eval_evcap_coco.sh eval_evcap_coco 0
bash scripts/eval_evcap_nocaps.sh eval_evcap_nocaps 0
bash scripts/eval_evcap_flickr30k.sh eval_evcap_flickr30k 0

where n denotes the ID of GPU used.

Acknowledgements

This repo is built on MiniGPT4 and ViECap, we thank the authors for their great effort.

Citation

If you find our work helpful for your research, please kindly consider citing:

@article{li2024evcap,
  title={EVCap: Retrieval-Augmented Image Captioning with External Visual-Name Memory for Open-World Comprehension}, 
  author={Jiaxuan Li and Duc Minh Vo and Akihiro Sugimoto and Hideki Nakayama},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month     = {June},
  year      = {2024},
}