Learning visual features for relational CBIR

@article{Messina2019LearningVF,
  title={Learning visual features for relational CBIR},
  author={N. Messina and G. Amato and Fabio Carrara and F. Falchi and C. Gennaro},
  journal={International Journal of Multimedia Information Retrieval},
  year={2019},
  volume={9},
  pages={113-124}
}
  • N. Messina, G. Amato, +2 authors C. Gennaro
  • Published 2019
  • Computer Science
  • International Journal of Multimedia Information Retrieval
  • Recent works in deep-learning research highlighted remarkable relational reasoning capabilities of some carefully designed architectures. In this work, we employ a relationship-aware deep learning model to extract compact visual features used relational image descriptors. In particular, we are interested in relational content-based image retrieval (R-CBIR), a task consisting in finding images containing similar inter-object relationships. Inspired by the relation networks (RN) employed in… CONTINUE READING
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    References

    SHOWING 1-10 OF 37 REFERENCES
    Learning Relationship-Aware Visual Features
    • 7
    • PDF
    R-VQA: Learning Visual Relation Facts with Semantic Attention for Visual Question Answering
    • 27
    • Highly Influential
    • PDF
    Exploring Visual Relationship for Image Captioning
    • 217
    • PDF
    Detecting Visual Relationships with Deep Relational Networks
    • Bo Dai, Yuqi Zhang, D. Lin
    • Computer Science
    • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
    • 2017
    • 233
    • PDF
    Attentive Relational Networks for Mapping Images to Scene Graphs
    • 37
    • PDF
    End-to-End Learning of Deep Visual Representations for Image Retrieval
    • 252
    • PDF
    Weakly-Supervised Learning of Visual Relations
    • 102
    • PDF
    A simple neural network module for relational reasoning
    • 814
    • PDF