This is the PyTorch Implementation for our ECCV 2022 paper Fine-Grained Visual Entailment.
Visual entailment is a recently proposed multimodal reasoning task where the goal is to predict the logical relationship of a piece of text to an image. In this paper, we propose an extension of this task, where the goal is to predict the logical relationship of fine-grained knowledge elements within a piece of text to an image. Unlike prior work, our method is inherently explainable and makes logical predictions at different levels of granularity. Because we lack fine-grained labels to train our method, we propose a novel multi-instance learning approach which learns a fine-grained labeling using only sample-level supervision. We also impose novel semantic structural constraints which ensure that fine-grained predictions are internally semantically consistent.
Check out INSTALL.md for installation instructions.
We provide our dataset, pre-extracted image features for Flickr30K, and model checkpoints. Please see DOWNLOAD.md for details.
To replicate our results, please see RUN.md for details.
If you want to use custom data, check out DATA.md for details on data formats.
Our code builds upon microsoft/Oscar.
This research is based upon work supported by DARPA SemaFor Program No. HR001120C0123. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of DARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation.
@article{thomas2022fine,
title={Fine-Grained Visual Entailment},
author={Thomas, Christopher and Zhang, Yipeng and Chang, Shih-Fu},
journal={ECCV},
year={2022}
}