@inproceedings{li-harrison-2021-error,
title = "Error Causal inference for Multi-Fusion models",
author = "Li, Chengxi and
Harrison, Brent",
editor = "{Xin} and
Hu, Ronghang and
Hudson, Drew and
Fu, Tsu-Jui and
Rohrbach, Marcus and
Fried, Daniel",
booktitle = "Proceedings of the Second Workshop on Advances in Language and Vision Research",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.alvr-1.2",
doi = "10.18653/v1/2021.alvr-1.2",
pages = "11--15",
abstract = "In this paper, we propose an error causal inference method that could be used for finding dominant features for a faulty instance under a well-trained multi-modality input model, which could apply to any testing instance. We evaluate our method using a well-trained multi-modalities stylish caption generation model and find those causal inferences that could provide us the insights for next step optimization.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="li-harrison-2021-error">
<titleInfo>
<title>Error Causal inference for Multi-Fusion models</title>
</titleInfo>
<name type="personal">
<namePart type="given">Chengxi</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Brent</namePart>
<namePart type="family">Harrison</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2021-06</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Second Workshop on Advances in Language and Vision Research</title>
</titleInfo>
<name>
<namePart>Xin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ronghang</namePart>
<namePart type="family">Hu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Drew</namePart>
<namePart type="family">Hudson</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tsu-Jui</namePart>
<namePart type="family">Fu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Marcus</namePart>
<namePart type="family">Rohrbach</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Daniel</namePart>
<namePart type="family">Fried</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Online</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>In this paper, we propose an error causal inference method that could be used for finding dominant features for a faulty instance under a well-trained multi-modality input model, which could apply to any testing instance. We evaluate our method using a well-trained multi-modalities stylish caption generation model and find those causal inferences that could provide us the insights for next step optimization.</abstract>
<identifier type="citekey">li-harrison-2021-error</identifier>
<identifier type="doi">10.18653/v1/2021.alvr-1.2</identifier>
<location>
<url>https://aclanthology.org/2021.alvr-1.2</url>
</location>
<part>
<date>2021-06</date>
<extent unit="page">
<start>11</start>
<end>15</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Error Causal inference for Multi-Fusion models
%A Li, Chengxi
%A Harrison, Brent
%Y Hu, Ronghang
%Y Hudson, Drew
%Y Fu, Tsu-Jui
%Y Rohrbach, Marcus
%Y Fried, Daniel
%E Xin
%S Proceedings of the Second Workshop on Advances in Language and Vision Research
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F li-harrison-2021-error
%X In this paper, we propose an error causal inference method that could be used for finding dominant features for a faulty instance under a well-trained multi-modality input model, which could apply to any testing instance. We evaluate our method using a well-trained multi-modalities stylish caption generation model and find those causal inferences that could provide us the insights for next step optimization.
%R 10.18653/v1/2021.alvr-1.2
%U https://aclanthology.org/2021.alvr-1.2
%U https://doi.org/10.18653/v1/2021.alvr-1.2
%P 11-15
Markdown (Informal)
[Error Causal inference for Multi-Fusion models](https://aclanthology.org/2021.alvr-1.2) (Li & Harrison, ALVR 2021)
ACL
- Chengxi Li and Brent Harrison. 2021. Error Causal inference for Multi-Fusion models. In Proceedings of the Second Workshop on Advances in Language and Vision Research, pages 11–15, Online. Association for Computational Linguistics.