Fuxiao Liu, Yinghan Wang, Tianlu Wang, Vicente Ordonez (EMNLP 2021)
We propose Visual News Captioner, an entity-aware model for the task of news image captioning. We also introduce Visual News, a large-scale benchmark consisting of more than one million news images along with associated news articles, image captions, author information, and other metadata. Unlike the standard image captioning task, news images depict situations where people, locations, and events are of paramount importance. Our proposed method can effectively combine visual and textual features to generate captions with richer information such as events and entities. More specifically, built upon the Transformer architecture, our model is further equipped with novel multi-modal feature fusion techniques and attention mechanisms, which are designed to generate named entities more accurately. Our method utilizes much fewer parameters while achieving slightly better prediction results than competing methods. Our larger and more diverse Visual News dataset further highlights the remaining challenges in captioning news images.
@misc{liu2020visualnews,
title={VisualNews : Benchmark and Challenges in Entity-aware Image Captioning},
author={Fuxiao Liu and Yinghan Wang and Tianlu Wang and Vicente Ordonez},
year={2020},
eprint={2010.03743},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
}
- [03/13]🔥 Our paper "MMC: Advancing Multimodal Chart Understanding with LLM Instruction Tuning" is accepted to NAACL 2024.
- [02/26]🔥 Our paper "HallusionBench: You See What You Think? Or You Think What You See? An Image-Context Reasoning Benchmark Challenging for GPT-4V(ision), LLaVA-1.5, and Other Multi-modality Models" is accpeted to CVPR 2024.
- [01/15]🔥 Our paper Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning is accepted by ICLR 2024
- Our dataset is available upon request. Please contact fl3es@umd.edu
- To access our dataset, please refer to this demo
- pytorch==1.0.0
- spacy==2.0.11
- h5py==2.7.0
- bs4==4.5.3
- joblib==0.12.2
- nltk==3.2.3
- tqdm==4.19.5
- urllib2==2.7
- goose==1.0.25
The code of our model is in ./model.
CUDA_VISIBLE_DEVICES=0 python main.py
If you have any questions, please email: fl3es@umd.edu
If you find our paper/code useful, please consider citing: