Learning visual features for relational CBIR

@article{Messina2019LearningVF,
  title={Learning visual features for relational CBIR},
  author={Nicola Messina and Giuseppe Amato and Fabio Carrara and Fabrizio Falchi and Claudio Gennaro},
  journal={International Journal of Multimedia Information Retrieval},
  year={2019},
  pages={1 - 12}
}
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

References

Publications referenced by this paper.
SHOWING 1-10 OF 30 REFERENCES

Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations

  • International Journal of Computer Vision
  • 2016
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL