title: Can OOD Object Detectors Learn from Foundation Models?
summary: Out-of-distribution (OOD) object detection is a challenging task due to the
  absence of open-set OOD data. Inspired by recent advancements in text-to-image
  generative models, such as Stable Diffusion, we study the potential of
  generative models trained on large-scale open-set data to synthesize OOD
  samples, thereby enhancing OOD object detection. We introduce SyncOOD, a simple
  data curation method that capitalizes on the capabilities of large foundation
  models to automatically extract meaningful OOD data from text-to-image
  generative models. This offers the model access to open-world knowledge
  encapsulated within off-the-shelf foundation models. The synthetic OOD samples
  are then employed to augment the training of a lightweight, plug-and-play OOD
  detector, thus effectively optimizing the in-distribution (ID)/OOD decision
  boundaries. Extensive experiments across multiple benchmarks demonstrate that
  SyncOOD significantly outperforms existing methods, establishing new
  state-of-the-art performance with minimal synthetic data usage.
paper_authors._id: 66e009815511a2f2f734f895
paper_authors.name: Jiahui Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-12T07:13:31.773Z
paper_authors._id: 654bc1fa7824e2bb58d2c455
paper_authors.avatarUrl: /avatars/55c6e4f5e1f6ce0ceada6d5f0c271d45.svg
paper_authors.fullname: Jiahui Liu
paper_authors.type: user
paper_authors.user: jliu-ac
paper_authors._id: 66e009815511a2f2f734f896
paper_authors.name: Xin Wen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-12T08:13:39.208Z
paper_authors._id: 63483629ac5172169929da0e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1665676793089-noauth.jpeg
paper_authors.fullname: Xin Wen
paper_authors.type: user
paper_authors.user: xwen99
paper_authors._id: 66e009815511a2f2f734f897
paper_authors.name: Shizhen Zhao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-16T07:06:17.825Z
paper_authors._id: 64bfd136e6099755408a628c
paper_authors.avatarUrl: /avatars/e1c7bd7b06ea5e7783341052a82e0461.svg
paper_authors.fullname: Shizhen ZHAO
paper_authors.type: user
paper_authors.user: HenryxZHAO
paper_authors._id: 66e009815511a2f2f734f898
paper_authors.name: Yingxian Chen
paper_authors._id: 66e009815511a2f2f734f899
paper_authors.name: Xiaojuan Qi
paper_title: Can OOD Object Detectors Learn from Foundation Models?
paper_summary: Out-of-distribution (OOD) object detection is a challenging task due to the
  absence of open-set OOD data. Inspired by recent advancements in text-to-image
  generative models, such as Stable Diffusion, we study the potential of
  generative models trained on large-scale open-set data to synthesize OOD
  samples, thereby enhancing OOD object detection. We introduce SyncOOD, a simple
  data curation method that capitalizes on the capabilities of large foundation
  models to automatically extract meaningful OOD data from text-to-image
  generative models. This offers the model access to open-world knowledge
  encapsulated within off-the-shelf foundation models. The synthetic OOD samples
  are then employed to augment the training of a lightweight, plug-and-play OOD
  detector, thus effectively optimizing the in-distribution (ID)/OOD decision
  boundaries. Extensive experiments across multiple benchmarks demonstrate that
  SyncOOD significantly outperforms existing methods, establishing new
  state-of-the-art performance with minimal synthetic data usage.
paper_ai_keywords: Out-of-distribution (OOD) object detection text-to-image generative models Stable Diffusion open-set OOD data foundation models SyncOOD data curation method OOD samples lightweight, plug-and-play OOD detector in-distribution (ID)/OOD decision boundaries state-of-the-art performance