{"type":"ScholarlyArticle","title":"KTCN: Enhancing Open-World Object Detection with Knowledge Transfer and Class-Awareness Neutralization","authors":[{"name":"Xing Xi","slug":"xing-xi","given":"Xing","family":"Xi","affiliation":"South China University of Technology"},{"name":"Yangyang Huang","slug":"yangyang-huang","given":"Yangyang","family":"Huang","affiliation":"South China University of Technology"},{"name":"Jinhao Lin","slug":"jinhao-lin","given":"Jinhao","family":"Lin","affiliation":"South China University of Technology"},{"name":"Ronghua Luo","slug":"ronghua-luo","given":"Ronghua","family":"Luo","affiliation":"South China University of Technology"}],"abstract":"Open-World Object Detection (OWOD) has garnered widespread attention due to its ability to recall unannotated objects. Existing works generate pseudo-labels for the model using heuristic priors, which limits the model’s performance. In this paper, we leverage the knowledge of the large-scale visual model to provide supervision for unknown categories. Specifically, we use the Segment Anything Model (SAM) to generate raw pseudo-labels for potential objects and refine them through Intersection over Union (IOU) and the shortest bounding box side length. Nevertheless, the abundance of pseudo-labels still exacerbates the competition issue in the one-to-many label assignment. To address this, we propose the Dual Matching Label Assignment (DMLA) strategy. Furthermore, we propose the Class-Awareness Neutralizer (CAN) to reduce the model’s bias towards known categories. Evaluation results on open-world object detection benchmarks, including MS COCO and Pascal VOC, show that our method achieves nearly 200% the unknown recall rate of previous state-of-the-art (SOTA) methods, reaching 41.5 U-Recall. Additionally, our approach does not add any extra parameters, maintaining the inference speed advantage of Faster R-CNN, leading the SOTA methods based on deformable DETR at a speed of over 10 FPS. Our code is available at https://github.com/xxyzll/KTCN.","doi":"10.24963/ijcai.2024/162","url":"https://ijcai.org/proceedings/2024/162","pdf":"https://ijcai.org/proceedings/2024/0162.pdf","markdown":"https://ijcai.org/proceedings/2024/162.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/162","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":162,"pages":{"first":1462,"last":1470},"keywords":["Computer Vision: CV: Recognition (object detection, categorization)"],"videos":[],"references":[{"position":1,"raw":"[Dhamija et al., 2020] Akshay Dhamija, Manuel Gunther, Jonathan Ventura, and Terrance Boult. 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