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  • Memory-Based Network for Scene Graph with Unbalanced Relations

    Author(s)
    Wang, W
    Liu, R
    Wang, M
    Wang, S
    Chang, X
    Chen, Y
    Griffith University Author(s)
    Wang, Sen
    Year published
    2020
    Metadata
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    Abstract
    The scene graph which can be represented by a set of visual triples is composed of objects and the relations between object pairs. It is vital for image captioning, visual question answering, and many other applications. However, there is a long tail distribution on the scene graph dataset, and the tail relation cannot be accurately identified due to the lack of training samples. The problem of the nonstandard label and feature overlap on the scene graph affects the extraction of discriminative features and exacerbates the effect of data imbalance on the model. For these reasons, we propose a novel scene graph generation ...
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    The scene graph which can be represented by a set of visual triples is composed of objects and the relations between object pairs. It is vital for image captioning, visual question answering, and many other applications. However, there is a long tail distribution on the scene graph dataset, and the tail relation cannot be accurately identified due to the lack of training samples. The problem of the nonstandard label and feature overlap on the scene graph affects the extraction of discriminative features and exacerbates the effect of data imbalance on the model. For these reasons, we propose a novel scene graph generation model that can effectively improve the detection of low-frequency relations. We use the method of memory features to realize the transfer of high-frequency relation features to low-frequency relation features. Extensive experiments on scene graph datasets show that our model significantly improved the performance of two evaluation metrics R@K and mR@K compared with state-of-the-art baselines.
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    Conference Title
    MM '20: Proceedings of the 28th ACM International Conference on Multimedia
    DOI
    https://doi.org/10.1145/3394171.3413507
    Subject
    Artificial Intelligence and Image Processing
    Networking and Communications
    Publication URI
    http://hdl.handle.net/10072/402713
    Collection
    • Conference outputs

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