title: EmerDiff: Emerging Pixel-level Semantic Knowledge in Diffusion Models
summary: Diffusion models have recently received increasing research attention for
  their remarkable transfer abilities in semantic segmentation tasks. However,
  generating fine-grained segmentation masks with diffusion models often requires
  additional training on annotated datasets, leaving it unclear to what extent
  pre-trained diffusion models alone understand the semantic relations of their
  generated images. To address this question, we leverage the semantic knowledge
  extracted from Stable Diffusion (SD) and aim to develop an image segmentor
  capable of generating fine-grained segmentation maps without any additional
  training. The primary difficulty stems from the fact that semantically
  meaningful feature maps typically exist only in the spatially lower-dimensional
  layers, which poses a challenge in directly extracting pixel-level semantic
  relations from these feature maps. To overcome this issue, our framework
  identifies semantic correspondences between image pixels and spatial locations
  of low-dimensional feature maps by exploiting SD's generation process and
  utilizes them for constructing image-resolution segmentation maps. In extensive
  experiments, the produced segmentation maps are demonstrated to be well
  delineated and capture detailed parts of the images, indicating the existence
  of highly accurate pixel-level semantic knowledge in diffusion models.
paper_authors._id: 65af54a6c92e09d5cdd088a2
paper_authors.name: Koichi Namekata
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-01-23T08:08:34.475Z
paper_authors._id: 65aec2b6101482afcc3126f2
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/65aec2b6101482afcc3126f2/IEKT6u1Upx2clt7qhhoUB.jpeg
paper_authors.fullname: Koichi Namekata
paper_authors.type: user
paper_authors.user: kmcode
paper_authors._id: 65af54a6c92e09d5cdd088a3
paper_authors.name: Amirmojtaba Sabour
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-01-23T17:09:20.093Z
paper_authors._id: 656015f28138827138c70858
paper_authors.avatarUrl: /avatars/b8471ae4d80f078c7c928fc3d8f49126.svg
paper_authors.fullname: Amirmojtaba Sabour
paper_authors.type: user
paper_authors.user: amsabour
paper_authors._id: 65af54a6c92e09d5cdd088a4
paper_authors.name: Sanja Fidler
paper_authors._id: 65af54a6c92e09d5cdd088a5
paper_authors.name: Seung Wook Kim
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-23T10:40:57.253Z
paper_authors._id: 64fb5827d82fc6977d70582d
paper_authors.avatarUrl: /avatars/54180874d2f162572c414970b8750759.svg
paper_authors.fullname: Seung Wook Kim
paper_authors.type: user
paper_authors.user: swkimbd
paper_title: EmerDiff: Emerging Pixel-level Semantic Knowledge in Diffusion Models
paper_summary: Diffusion models have recently received increasing research attention for
  their remarkable transfer abilities in semantic segmentation tasks. However,
  generating fine-grained segmentation masks with diffusion models often requires
  additional training on annotated datasets, leaving it unclear to what extent
  pre-trained diffusion models alone understand the semantic relations of their
  generated images. To address this question, we leverage the semantic knowledge
  extracted from Stable Diffusion (SD) and aim to develop an image segmentor
  capable of generating fine-grained segmentation maps without any additional
  training. The primary difficulty stems from the fact that semantically
  meaningful feature maps typically exist only in the spatially lower-dimensional
  layers, which poses a challenge in directly extracting pixel-level semantic
  relations from these feature maps. To overcome this issue, our framework
  identifies semantic correspondences between image pixels and spatial locations
  of low-dimensional feature maps by exploiting SD's generation process and
  utilizes them for constructing image-resolution segmentation maps. In extensive
  experiments, the produced segmentation maps are demonstrated to be well
  delineated and capture detailed parts of the images, indicating the existence
  of highly accurate pixel-level semantic knowledge in diffusion models.
paper_ai_summary: Researchers develop a fine-grained image segmentor using Stable Diffusion without additional training, extracting semantic correspondences from low-dimensional feature maps for pixel-level segmentation.
paper_ai_keywords: diffusion models semantic segmentation tasks Stable Diffusion (SD) image segmentor fine-grained segmentation maps semantic knowledge low-dimensional feature maps semantic correspondences pixel-level semantic relations