基於基礎模型，能否從OOD對象檢測器中學習？

Can OOD Object Detectors Learn from Foundation Models?

September 8, 2024

作者: Jiahui Liu, Xin Wen, Shizhen Zhao, Yingxian Chen, Xiaojuan Qi

cs.AI

摘要

在分布外（OOD）物體檢測是一項具有挑戰性的任務，因為缺乏開放式分布外數據。受到最近文本到圖像生成模型（如穩定擴散）的進展的啟發，我們研究了在大規模開放式數據上訓練的生成模型潛力，以合成OOD樣本，從而增強OOD物體檢測。我們引入了SyncOOD，一種簡單的數據整理方法，利用大型基礎模型的能力，從文本到圖像生成模型中自動提取有意義的OOD數據。這為模型提供了訪問即時基礎模型中封裝的開放世界知識的能力。然後，利用合成的OOD樣本來擴充輕量、即插即用的OOD檢測器的訓練，從而有效地優化分布內（ID）/OOD的決策邊界。通過多個基準測試的廣泛實驗表明，SyncOOD明顯優於現有方法，以最小的合成數據使用量建立了新的最先進性能。

English

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.