mlsanityData to 14 Oct 2026

A Self-Explainable Stylish Image Captioning Framework via Multi-References

arXiv 2110.10704 ↗First posted 20 Oct 2021cs.CLcited by 0

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Abstract (arXiv)

In this paper, we propose to build a stylish image captioning model through a Multi-style Multi modality mechanism (2M). We demonstrate that with 2M, we can build an effective stylish captioner and that multi-references produced by the model can also support explaining the model through identifying erroneous input features on faulty examples. We show how this 2M mechanism can be used to build stylish captioning models and show how these models can be utilized to provide explanations of likely errors in the models.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 24 references that are in the map. First posted on arXiv 20 Oct 2021.

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References · 11

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,546
  3. 2014CIDEr: Consensus-based Image Description Evaluationcited by 2,046
  4. 2015Show, Attend and Tell: Neural Image Caption Generation with Visual Attentioncited by 1,423
  5. 2017Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answeringcited by 1,312
  6. 2020Oscar: Object-Semantics Aligned Pre-training for Vision-Language Taskscited by 1,214
  7. 2016SPICE: Semantic Propositional Image Caption Evaluationcited by 824
  8. 2015YFCC100M: The New Data in Multimedia Researchcited by 235
  9. 2015DenseCap: Fully Convolutional Localization Networks for Dense Captioningcited by 176
  10. 2018Engaging Image Captioning Via Personalitycited by 51
  11. 2018"Factual" or "Emotional": Stylized Image Captioning with Adaptive Learning and Attentioncited by 26

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