Scene Graph Generation Strategy with Co-occurrence Knowledge and Learnable Term Frequency
<|reference_start|>Scene Graph Generation Strategy with Co-occurrence Knowledge and Learnable Term Frequency: Scene graph generation (SGG) is an important task in image understanding because it represents the relationships between objects in an image as a graph structure, making it possible to understand the semantic relationships between objects intuitively. Previous SGG studies used a message-passing neural networks (MPNN) to update features, which can effectively reflect information about surrounding objects. However, these studies have failed to reflect the co-occurrence of objects during SGG generation. In addition, they only addressed the long-tail problem of the training dataset from the perspectives of sampling and learning methods. To address these two problems, we propose CooK, which reflects the Co-occurrence Knowledge between objects, and the learnable term frequency-inverse document frequency (TF-l-IDF) to solve the long-tail problem. We applied the proposed model to the SGG benchmark dataset, and the results showed a performance improvement of up to 3.8% compared with existing state-of-the-art models in SGGen subtask. The proposed method exhibits generalization ability from the results obtained, showing uniform performance improvement for all MPNN models.<|reference_end|>
@article{kim2024scene,
    title={Scene Graph Generation Strategy with Co-occurrence Knowledge and
  Learnable Term Frequency},
    author={Hyeongjin Kim, Sangwon Kim, Dasom Ahn, Jong Taek Lee, Byoung Chul Ko},
    journal={arXiv preprint arXiv:2405.12648},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.12648},
    primaryClass={cs.CV cs.AI}
}