title: StableSemantics: A Synthetic Language-Vision Dataset of Semantic
    Representations in Naturalistic Images
summary: Understanding the semantics of visual scenes is a fundamental challenge in
  Computer Vision. A key aspect of this challenge is that objects sharing similar
  semantic meanings or functions can exhibit striking visual differences, making
  accurate identification and categorization difficult. Recent advancements in
  text-to-image frameworks have led to models that implicitly capture natural
  scene statistics. These frameworks account for the visual variability of
  objects, as well as complex object co-occurrences and sources of noise such as
  diverse lighting conditions. By leveraging large-scale datasets and
  cross-attention conditioning, these models generate detailed and contextually
  rich scene representations. This capability opens new avenues for improving
  object recognition and scene understanding in varied and challenging
  environments. Our work presents StableSemantics, a dataset comprising 224
  thousand human-curated prompts, processed natural language captions, over 2
  million synthetic images, and 10 million attention maps corresponding to
  individual noun chunks. We explicitly leverage human-generated prompts that
  correspond to visually interesting stable diffusion generations, provide 10
  generations per phrase, and extract cross-attention maps for each image. We
  explore the semantic distribution of generated images, examine the distribution
  of objects within images, and benchmark captioning and open vocabulary
  segmentation methods on our data. To the best of our knowledge, we are the
  first to release a diffusion dataset with semantic attributions. We expect our
  proposed dataset to catalyze advances in visual semantic understanding and
  provide a foundation for developing more sophisticated and effective visual
  models. Website: https://stablesemantics.github.io/StableSemantics
paper_authors._id: 6675d0a88794bbd33ffb1adb
paper_authors.name: Rushikesh Zawar
paper_authors._id: 6675d0a88794bbd33ffb1adc
paper_authors.name: Shaurya Dewan
paper_authors._id: 6675d0a88794bbd33ffb1add
paper_authors.name: Andrew F. Luo
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-06-21T19:15:43.344Z
paper_authors._id: 64b6ce23dbbd1f2cdb624d56
paper_authors.avatarUrl: /avatars/371abdcd68e64f0430cbdbf3b69e1645.svg
paper_authors.fullname: Andrew Luo
paper_authors.type: user
paper_authors.user: aluo-x
paper_authors._id: 6675d0a88794bbd33ffb1ade
paper_authors.name: Margaret M. Henderson
paper_authors._id: 6675d0a88794bbd33ffb1adf
paper_authors.name: Michael J. Tarr
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-06-21T19:12:44.405Z
paper_authors._id: 649c2cdc92525b3ac9fe4f98
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/E4QRz0o81ROM4GyYo-xZN.jpeg
paper_authors.fullname: Michael J Tarr
paper_authors.type: user
paper_authors.user: Academic-Mercenary
paper_authors._id: 6675d0a88794bbd33ffb1ae0
paper_authors.name: Leila Wehbe
paper_title: StableSemantics: A Synthetic Language-Vision Dataset of Semantic
    Representations in Naturalistic Images
paper_summary: Understanding the semantics of visual scenes is a fundamental challenge in
  Computer Vision. A key aspect of this challenge is that objects sharing similar
  semantic meanings or functions can exhibit striking visual differences, making
  accurate identification and categorization difficult. Recent advancements in
  text-to-image frameworks have led to models that implicitly capture natural
  scene statistics. These frameworks account for the visual variability of
  objects, as well as complex object co-occurrences and sources of noise such as
  diverse lighting conditions. By leveraging large-scale datasets and
  cross-attention conditioning, these models generate detailed and contextually
  rich scene representations. This capability opens new avenues for improving
  object recognition and scene understanding in varied and challenging
  environments. Our work presents StableSemantics, a dataset comprising 224
  thousand human-curated prompts, processed natural language captions, over 2
  million synthetic images, and 10 million attention maps corresponding to
  individual noun chunks. We explicitly leverage human-generated prompts that
  correspond to visually interesting stable diffusion generations, provide 10
  generations per phrase, and extract cross-attention maps for each image. We
  explore the semantic distribution of generated images, examine the distribution
  of objects within images, and benchmark captioning and open vocabulary
  segmentation methods on our data. To the best of our knowledge, we are the
  first to release a diffusion dataset with semantic attributions. We expect our
  proposed dataset to catalyze advances in visual semantic understanding and
  provide a foundation for developing more sophisticated and effective visual
  models. Website: https://stablesemantics.github.io/StableSemantics
paper_ai_keywords: text-to-image frameworks natural scene statistics visual variability object co-occurrences cross-attention conditioning scene representations object recognition scene understanding StableSemantics human-curated prompts processed natural language captions synthetic images attention maps visually interesting stable diffusion generations captioning open vocabulary segmentation diffusion dataset semantic attributions visual semantic understanding