Towards Adaptive Open-Set Object Detection via Category-Level Collaboration Knowledge Mining

Existing object detection methods struggle to generalize across increasingly data domains while simultaneously adapting to the emergence of novel categories. To tackle this challenge, adaptive open-set object detection (AOOD) has been introduced, which employs supervised training on base categories within the source domain while enabling unsupervised adaptation to both base and novel categories in the target domain. However, existing AOOD approaches are still hindered by several limitations, including insufficient cross-domain feature representation, inter-category ambiguity in novel classes, and inherent feature bias toward the source domain. To overcome these issues, this paper proposes a category-level collaboration knowledge mining strategy designed to comprehensively exploit both inter-class and intra-class feature relationships across domains. Specifically, a clustering-based memory bank (CMB) is initially constructed to aggregate class prototype features, class auxiliary features, and intra-class disparity features, thereby embedding rich category-level knowledge into a unified memory structure. The CMB is iteratively updated through unsupervised clustering, which facilitates the modeling of intra-category relationships and enhances its capacity for cross-domain knowledge representation. Subsequently, a base-to-novel selection metric (BNSM) is designed to identify features corresponding to novel categories within the source domain by regulating the relationships between the novel categories and each base category. The selected features are then leveraged to initialize the object detector for the classification of novel categories. Finally, an adaptive feature assignment (AFA) strategy is introduced to transfer the learned category-level knowledge to the target domain, enabling the assignment of category labels to features. The memory bank is updated asynchronously with these assigned features to mitigate source domain bias. Extensive experiments conducted on diverse domain datasets demonstrate that the proposed method consistently outperforms state-of-the-art AOOD approaches, achieving performance gains of 1.1 to 5.5 mAP. Code is available at https://github.com/Jandsome/CCKM.