Can OOD Object Detectors Learn from Foundation Models?
Authors: name: Jiahui Liu, name: Xin Wen, name: Shizhen Zhao, name: Yingxian Chen, name: Xiaojuan Qi

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.