Can OOD Object Detectors Learn from Foundation Models?
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.
66e009815511a2f2f734f895
Jiahui Liu
claimed_verified
2024-09-12T07:13:31.773Z
654bc1fa7824e2bb58d2c455
/avatars/55c6e4f5e1f6ce0ceada6d5f0c271d45.svg
Jiahui Liu
user
jliu-ac
66e009815511a2f2f734f896
Xin Wen
claimed_verified
2024-09-12T08:13:39.208Z
63483629ac5172169929da0e
https://cdn-avatars.huggingface.co/v1/production/uploads/1665676793089-noauth.jpeg
Xin Wen
user
xwen99
66e009815511a2f2f734f897
Shizhen Zhao
admin_assigned
2024-09-16T07:06:17.825Z
64bfd136e6099755408a628c
/avatars/e1c7bd7b06ea5e7783341052a82e0461.svg
Shizhen ZHAO
user
HenryxZHAO
66e009815511a2f2f734f898
Yingxian Chen
66e009815511a2f2f734f899
Xiaojuan Qi
claimed_verified
2025-07-15T19:14:39.161Z
6875266f9cd3191dfddc7071
/avatars/64c581910833b111e9a7bae5b8740229.svg
xiaojuan qi
user
xjqi
Can OOD Object Detectors Learn from Foundation Models?
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.
SyncOOD uses generative models to create synthetic OOD samples, enhancing OOD object detection by optimally setting ID/OOD decision boundaries.
generative models out-of-distribution (OOD) object detection open-set OOD data Stable Diffusion text-to-image generative models SyncOOD data curation foundation models synthetic OOD samples lightweight plug-and-play OOD detector