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        <title>Learning to Describe Differences Between Pairs of Similar Images</title>
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        <namePart type="given">Harsh</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
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    <abstract>In this paper, we introduce the task of automatically generating text to describe the differences between two similar images. We collect a new dataset by crowd-sourcing difference descriptions for pairs of image frames extracted from video-surveillance footage. Annotators were asked to succinctly describe all the differences in a short paragraph. As a result, our novel dataset provides an opportunity to explore models that align language and vision, and capture visual salience. The dataset may also be a useful benchmark for coherent multi-sentence generation. We perform a first-pass visual analysis that exposes clusters of differing pixels as a proxy for object-level differences. We propose a model that captures visual salience by using a latent variable to align clusters of differing pixels with output sentences. We find that, for both single-sentence generation and as well as multi-sentence generation, the proposed model outperforms the models that use attention alone.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1436</identifier>
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%0 Conference Proceedings
%T Learning to Describe Differences Between Pairs of Similar Images
%A Jhamtani, Harsh
%A Berg-Kirkpatrick, Taylor
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F jhamtani-berg-kirkpatrick-2018-learning
%X In this paper, we introduce the task of automatically generating text to describe the differences between two similar images. We collect a new dataset by crowd-sourcing difference descriptions for pairs of image frames extracted from video-surveillance footage. Annotators were asked to succinctly describe all the differences in a short paragraph. As a result, our novel dataset provides an opportunity to explore models that align language and vision, and capture visual salience. The dataset may also be a useful benchmark for coherent multi-sentence generation. We perform a first-pass visual analysis that exposes clusters of differing pixels as a proxy for object-level differences. We propose a model that captures visual salience by using a latent variable to align clusters of differing pixels with output sentences. We find that, for both single-sentence generation and as well as multi-sentence generation, the proposed model outperforms the models that use attention alone.
%R 10.18653/v1/D18-1436
%U https://aclanthology.org/D18-1436/
%U https://doi.org/10.18653/v1/D18-1436
%P 4024-4034

Markdown (Informal)

[Learning to Describe Differences Between Pairs of Similar Images](https://aclanthology.org/D18-1436/) (Jhamtani & Berg-Kirkpatrick, EMNLP 2018)

• Learning to Describe Differences Between Pairs of Similar Images (Jhamtani & Berg-Kirkpatrick, EMNLP 2018)

ACL

• Harsh Jhamtani and Taylor Berg-Kirkpatrick. 2018. Learning to Describe Differences Between Pairs of Similar Images. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4024–4034, Brussels, Belgium. Association for Computational Linguistics.