Generative Representation and Discriminative Classification for Few-shot Open-set Object Detection

Peixue Shen, Ruoqi Li, Yan Luo, Yiru Zhao, Chao Gao, Chongyang Zhang

Abstract: Open-Set Object Detection (OSOD) aims to train detectors on closed-set datasets to detect known objects and identify unknown objects in open-set conditions. Traditional discriminative classifier-based OSOD methods struggle to accurately learn the decision boundary between known and unknown classes, often resulting in the misclassification of unknown samples. In this work, we aim to combine generative representation with discriminative classification to alleviate the issue of misclassification by transforming known-unknown recognition into a binary classification problem. The proposed two-stage OSOD approach proceeds as follows: during the generative representation stage, we employ Class-Conditioned Normalizing Flow (CCNF) to establish distribution mapping for each known category; In the discriminative classification stage, by utilizing a small number of unknown class samples, semi-push-pull supervised learning and entropy contrast learning are used to separate known and unknown classes. Extensive experiments demonstrate that our method significantly enhances OSOD performance, evidenced by a 25.8%-28.6% reduction in the Wilderness Index and a decrease of 4391-8870 units in Absolute Open-Set Errors on the test set VOC-COCO-T1.

External IDs:dblp:conf/vcip/ShenLLZGZ24