"What do Models Learn From Training on More Than Text? Measuring Visual Commonsense Knowledge".
This repo contains the code for the paper.
The repo is segmented into three main parts:
bert-clip-bert-train
, bert-lxmert-train
and bert-lxmert-train-scratch
) and CLIP-BERT. This repo also contains necessary model weights and code for pretraining.models/data/model-weights
, this can be run independently from the other directories.models/data/model-weights
, this can be run independently from the other directories.Both the Memory Colors evaluation and the Visual Property Norms evaluation depend on pre-trained model weights for the models evaluated. Some of this pre-training needs to be done separately in models.
@inproceedings{hagstrom-johansson-2022-models,
title = "What do Models Learn From Training on More Than Text? Measuring Visual Commonsense Knowledge",
author = {Hagstr{\"o}m, Lovisa and
Johansson, Richard},
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-srw.19",
pages = "252--261",
abstract = "There are limitations in learning language from text alone. Therefore, recent focus has been on developing multimodal models. However, few benchmarks exist that can measure what language models learn about language from multimodal training. We hypothesize that training on a visual modality should improve on the visual commonsense knowledge in language models. Therefore, we introduce two evaluation tasks for measuring visual commonsense knowledge in language models (code publicly available at: github.com/lovhag/measure-visual-commonsense-knowledge) and use them to evaluate different multimodal models and unimodal baselines. Primarily, we find that the visual commonsense knowledge is not significantly different between the multimodal models and unimodal baseline models trained on visual text data.",
}
This project wouldn't be possible without the Centre for Speech, Language, and the Brain (CSLB) at the University of Cambridge, the Huggingface library and the LXMERT repo, we thank you for your work!