@inproceedings{suter-etal-2021-grounding,
title = "Grounding Plural Phrases: Countering Evaluation Biases by Individuation",
author = "Suter, Julia and
Parcalabescu, Letitia and
Frank, Anette",
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-1.4/",
doi = "10.18653/v1/2021.alvr-1.4",
pages = "22--28",
abstract = "Phrase grounding (PG) is a multimodal task that grounds language in images. PG systems are evaluated on well-known benchmarks, using Intersection over Union (IoU) as evaluation metric. This work highlights a disconcerting bias in the evaluation of grounded plural phrases, which arises from representing sets of objects as a union box covering all component bounding boxes, in conjunction with the IoU metric. We detect, analyze and quantify an evaluation bias in the grounding of plural phrases and define a novel metric, c-IoU, based on a union box`s component boxes. We experimentally show that our new metric greatly alleviates this bias and recommend using it for fairer evaluation of plural phrases in PG tasks."
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="suter-etal-2021-grounding">
<titleInfo>
<title>Grounding Plural Phrases: Countering Evaluation Biases by Individuation</title>
</titleInfo>
<name type="personal">
<namePart type="given">Julia</namePart>
<namePart type="family">Suter</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Letitia</namePart>
<namePart type="family">Parcalabescu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Anette</namePart>
<namePart type="family">Frank</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>Phrase grounding (PG) is a multimodal task that grounds language in images. PG systems are evaluated on well-known benchmarks, using Intersection over Union (IoU) as evaluation metric. This work highlights a disconcerting bias in the evaluation of grounded plural phrases, which arises from representing sets of objects as a union box covering all component bounding boxes, in conjunction with the IoU metric. We detect, analyze and quantify an evaluation bias in the grounding of plural phrases and define a novel metric, c-IoU, based on a union box‘s component boxes. We experimentally show that our new metric greatly alleviates this bias and recommend using it for fairer evaluation of plural phrases in PG tasks.</abstract>
<identifier type="citekey">suter-etal-2021-grounding</identifier>
<identifier type="doi">10.18653/v1/2021.alvr-1.4</identifier>
<location>
<url>https://aclanthology.org/2021.alvr-1.4/</url>
</location>
<part>
<date>2021-06</date>
<extent unit="page">
<start>22</start>
<end>28</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Grounding Plural Phrases: Countering Evaluation Biases by Individuation
%A Suter, Julia
%A Parcalabescu, Letitia
%A Frank, Anette
%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 suter-etal-2021-grounding
%X Phrase grounding (PG) is a multimodal task that grounds language in images. PG systems are evaluated on well-known benchmarks, using Intersection over Union (IoU) as evaluation metric. This work highlights a disconcerting bias in the evaluation of grounded plural phrases, which arises from representing sets of objects as a union box covering all component bounding boxes, in conjunction with the IoU metric. We detect, analyze and quantify an evaluation bias in the grounding of plural phrases and define a novel metric, c-IoU, based on a union box‘s component boxes. We experimentally show that our new metric greatly alleviates this bias and recommend using it for fairer evaluation of plural phrases in PG tasks.
%R 10.18653/v1/2021.alvr-1.4
%U https://aclanthology.org/2021.alvr-1.4/
%U https://doi.org/10.18653/v1/2021.alvr-1.4
%P 22-28
Markdown (Informal)
[Grounding Plural Phrases: Countering Evaluation Biases by Individuation](https://aclanthology.org/2021.alvr-1.4/) (Suter et al., ALVR 2021)
ACL