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VieCap4H-VLSP 2021: ObjectAoA-Enhancing performance of Object Relation Transformer with Attention on Attention for Vietnamese image captioning

arXiv 2211.05405 ↗First posted 10 Nov 2022cs.CV cs.CLcited by 0

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Abstract (arXiv)

Image captioning is currently a challenging task that requires the ability to both understand visual information and use human language to describe this visual information in the image. In this paper, we propose an efficient way to improve the image understanding ability of transformer-based method by extending Object Relation Transformer architecture with Attention on Attention mechanism. Experiments on the VieCap4H dataset show that our proposed method significantly outperforms its original structure on both the public test and private test of the Image Captioning shared task held by VLSP.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 22 references that are in the map. First posted on arXiv 10 Nov 2022.

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14 of 22 references.

References · 14

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  3. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  4. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  5. 2014CIDEr: Consensus-based Image Description Evaluationcited by 2,027
  6. 2015Show, Attend and Tell: Neural Image Caption Generation with Visual Attentioncited by 1,421
  7. 2017Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answeringcited by 1,310
  8. 2014Show and Tell: A Neural Image Caption Generatorcited by 877
  9. 2016Self-critical Sequence Training for Image Captioningcited by 544
  10. 2019Meshed-Memory Transformer for Image Captioningcited by 343
  11. 2019Attention on Attention for Image Captioningcited by 289
  12. 2019Image Captioning: Transforming Objects into Wordscited by 171
2 more
  1. 2020Image Captioning through Image Transformercited by 29
  2. 2020UIT-ViIC: A Dataset for the First Evaluation on Vietnamese Image Captioningcited by 13

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