mlsanityData to 7 Oct 2026

StyleM: Stylized Metrics for Image Captioning Built with Contrastive N-grams

arXiv 2201.00975 ↗First posted 4 Jan 2022cs.CV cs.AI cs.CLcited by 0

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Abstract (arXiv)

In this paper, we build two automatic evaluation metrics for evaluating the association between a machine-generated caption and a ground truth stylized caption: OnlyStyle and StyleCIDEr.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 25 references that are in the map. First posted on arXiv 4 Jan 2022.

Builds on

13 of 25 references.

References · 13

  1. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  2. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,540
  3. 2014CIDEr: Consensus-based Image Description Evaluationcited by 2,027
  4. 2015Show, Attend and Tell: Neural Image Caption Generation with Visual Attentioncited by 1,421
  5. 2017Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answeringcited by 1,310
  6. 2020Oscar: Object-Semantics Aligned Pre-training for Vision-Language Taskscited by 1,213
  7. 2016SPICE: Semantic Propositional Image Caption Evaluationcited by 820
  8. 2015LSTM: A Search Space Odysseycited by 315
  9. 2015YFCC100M: The New Data in Multimedia Researchcited by 234
  10. 2018Engaging Image Captioning Via Personalitycited by 51
  11. 2019Describing like humans: on diversity in image captioningcited by 46
  12. 2018"Factual" or "Emotional": Stylized Image Captioning with Adaptive Learning and Attentioncited by 26
1 more
  1. 20213M: Multi-style image caption generation using Multi-modality features under Multi-UPDOWN modelcited by 2

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