Towards OOD Object Detection With Unknown-Concept Guided Feature Diffusion

In general, learning plentiful knowledge corresponding to known objects is an important ability for humans. The unknown objects could be assumed to depart from the familiar knowledge. Inspired by this idea, we explore leveraging the extracted knowledge to reason a set of unknown concepts. And they could be used to address unsupervised out-of-distribution object detection (OOD-OD) that aims to detect unseen OOD objects without accessing any auxiliary OOD data during training. To this end, we propose a new approach, i.e., Unknown-Concept Guided Feature Diffusion (UCFD), including an object-related knowledge extractor and an unknown-concept guided diffusor for synthesizing virtual OOD features. Specifically, we define multiple learnable codewords to capture object-relevant visual knowledge from all object categories. To avoid the detection performance degradation of the in-distribution (ID) objects, these codewords are utilized to enhance object features. Next, an unknown-concept pool is constructed by mixing up these extracted codewords. Finally, to reduce the impact of lacking OOD data for supervision, we design an unknown-concept guided diffusor, which leverages the sampled unknown concepts from the pool to guide the reverse process to generate expected OOD features that deviate from the familiar knowledge. The significant performance gains on three different tasks demonstrate the superiorities of our method. Meanwhile, extensive visualization results show that our method could synthesize effective virtual OOD features.