Let's Go Shopping (LGS) -- Web-Scale Image-Text Dataset for Visual
  Concept Understanding
Vision and vision-language applications of neural networks, such as image
classification and captioning, rely on large-scale annotated datasets that
require non-trivial data-collecting processes. This time-consuming endeavor
hinders the emergence of large-scale datasets, limiting researchers and
practitioners to a small number of choices. Therefore, we seek more efficient
ways to collect and annotate images. Previous initiatives have gathered
captions from HTML alt-texts and crawled social media postings, but these data
sources suffer from noise, sparsity, or subjectivity. For this reason, we turn
to commercial shopping websites whose data meet three criteria: cleanliness,
informativeness, and fluency. We introduce the Let's Go Shopping (LGS) dataset,
a large-scale public dataset with 15 million image-caption pairs from publicly
available e-commerce websites. When compared with existing general-domain
datasets, the LGS images focus on the foreground object and have less complex
backgrounds. Our experiments on LGS show that the classifiers trained on
existing benchmark datasets do not readily generalize to e-commerce data, while
specific self-supervised visual feature extractors can better generalize.
Furthermore, LGS's high-quality e-commerce-focused images and bimodal nature
make it advantageous for vision-language bi-modal tasks: LGS enables
image-captioning models to generate richer captions and helps text-to-image
generation models achieve e-commerce style transfer.
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Yatong Bai
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Yatong Bai
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Utsav Garg
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Utsav Garg
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Apaar Shanker
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Apaar Shanker
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Haoming Zhang
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ZhangHaoMing
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Samyak Parajuli
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Samyak Parajuli
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Erhan Bas
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Erhan
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erhanbas
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Isidora Filipovic
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Amelia N. Chu
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Amelia Chu
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Eugenia D Fomitcheva
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Eug Fomitcheva
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Elliot Branson
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2024-01-10T09:19:46.659Z
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Elliot Branson
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Aerin Kim
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2024-01-10T03:27:05.979Z
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aerin kim
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Somayeh Sojoudi
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Kyunghyun Cho
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2024-01-10T09:20:04.684Z
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Kyunghyun Cho
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Let's Go Shopping (LGS) -- Web-Scale Image-Text Dataset for Visual
  Concept Understanding
Vision and vision-language applications of neural networks, such as image
classification and captioning, rely on large-scale annotated datasets that
require non-trivial data-collecting processes. This time-consuming endeavor
hinders the emergence of large-scale datasets, limiting researchers and
practitioners to a small number of choices. Therefore, we seek more efficient
ways to collect and annotate images. Previous initiatives have gathered
captions from HTML alt-texts and crawled social media postings, but these data
sources suffer from noise, sparsity, or subjectivity. For this reason, we turn
to commercial shopping websites whose data meet three criteria: cleanliness,
informativeness, and fluency. We introduce the Let's Go Shopping (LGS) dataset,
a large-scale public dataset with 15 million image-caption pairs from publicly
available e-commerce websites. When compared with existing general-domain
datasets, the LGS images focus on the foreground object and have less complex
backgrounds. Our experiments on LGS show that the classifiers trained on
existing benchmark datasets do not readily generalize to e-commerce data, while
specific self-supervised visual feature extractors can better generalize.
Furthermore, LGS's high-quality e-commerce-focused images and bimodal nature
make it advantageous for vision-language bi-modal tasks: LGS enables
image-captioning models to generate richer captions and helps text-to-image
generation models achieve e-commerce style transfer.
The Let's Go Shopping (LGS) dataset, containing 15 million image-caption pairs from clean, e-commerce websites, improves image classification and captioning performance, especially for vision-language tasks.
self-supervised visual feature extractors image-captioning models text-to-image generation models e-commerce style transfer