開放世界多維度特征融合場景圖生成算法研究

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中圖分類號：TP391 文獻標識碼：A

Open-world Multidimensional Feature Fusion Scene Graph Generation

GU Feifan1，ZHOU Mengmeng2，SONG Shimiao1，GE Jiashang 1 ，YANG Jie' （1.College of Mechanical and Electrical Engineering，Qingdao University，Qingdao 266o71，China; 2.Qingdao QCIT Technology Co.，Ltd.，Qingdao 266100，China）

Abstract： The open-world scene graph generation task has difficulty in detecting unknown objects and their relationships. To address this issue，a relation-reasoning model based on multidimensional feature fusion （MDFF） is proposed. The proposed model is combined with an open-world object detector to form a two-stage open-world scene graph generation algorithm. First，the pretrained open-world object detector identifies objects in the input images. The MDFF model then performs relationship inference based on the detection results. Comparative experiments are conducted on the VG -150 dataset using traditional methods and the MDFF model. The experimental results indicate that the MDFF model achieves 7% improvement in recall rate for predicate classification tasks. Moreover，the performance improves by 3% in open-world scene graph generation and zero-shot inference tasks. Furthermore，ablation studies have confirmed the effectiveness of different feature dimensions on model performance improvement.

Keywords： scene graph generation; feature fusion; object detection; deep learning

在開放世界環(huán)境中，場景圖生成任務復雜，特別是在未知場景和未見物體時，生成準確且具有高的泛化能力的場景圖成為研究的核心問題[1]。（剩余8352字）