Code to reproduce the results in "Visually Grounded Reasoning across Languages and Cultures", EMNLP 2021.

Last update: Nov 15, 2022

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Deep Learning marvl-code

Overview

marvl-code [WIP]

This is the implementation of the approaches described in the paper:

Fangyu Liu*, Emanuele Bugliarello*, Edoardo M. Ponti, Siva Reddy, Nigel Collier and Desmond Elliott. Visually Grounded Reasoning over Languages and Cultures. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), Nov 2021.

We provide the pretrained models and the code for reproducing our results.

The textual data and the preprocessed visual features are available in marvl-data.

The code will also be integrated into VOLTA, upon which our repository was origally built.

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Comments

Is img_segment_ids used for evaluation?

Hi @e-bug Is this useful information for the model? https://github.com/marvl-challenge/marvl-code/blob/master/volta/volta/datasets/marvl_dataset.py#L163 I saw you not using it

opened by lonestar234028 2
Excuse me, why can't I see the code?

Thank you very much for your interesting work, but after I found this website according to the link in the paper, I did not see the relevant code. Should I go to another website to get the code?

opened by Peng-weil 2
different results from iglue

Hello, I notice that the average result of zero-shot. in marvl-code mUNITER 54.0, xUNITER. 56.1 is higher than your recent work iglue mUNITER 53.72 xUNITER 54.59 . Especially for xUNITER ( 54.59 is about 1.5 points lower that 56.1) I reproduced your experiment with marvl-code Repository and the results are close to iglue . Can you give me some advice to imporve the results ? Or I should regard the iglue as baseline ?

opened by lizhiustc 1

Owner

GitHub

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Code to reproduce the results in "Visually Grounded Reasoning across Languages and Cultures", EMNLP 2021.

Related tags

Overview

marvl-code [WIP]

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Comments

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Excuse me, why can't I see the code?

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