mlsanityData to 14 Oct 2026

Automated Audio Captioning with Epochal Difficult Captions for Curriculum Learning

arXiv 2206.01918 ↗First posted 4 Jun 2022cs.CLcited by 0

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Abstract (arXiv)

In this paper, we propose an algorithm, Epochal Difficult Captions, to supplement the training of any model for the Automated Audio Captioning task. Epochal Difficult Captions is an elegant evolution to the keyword estimation task that previous work have used to train the encoder of the AAC model. Epochal Difficult Captions modifies the target captions based on a curriculum and a difficulty level determined as a function of current epoch. Epochal Difficult Captions can be used with any model architecture and is a lightweight function that does not increase training time. We test our results on three systems and show that using Epochal Difficult Captions consistently improves performance

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 21 references that are in the map. First posted on arXiv 4 Jun 2022.

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13 of 21 references.

References · 13

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  2. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  3. 2014CIDEr: Consensus-based Image Description Evaluationcited by 2,046
  4. 2019SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognitioncited by 1,380
  5. 2016SPICE: Semantic Propositional Image Caption Evaluationcited by 824
  6. 2019Clotho: An Audio Captioning Datasetcited by 358
  7. 2021Audio Captioning Transformercited by 55
  8. 2020Audio Captioning using Pre-Trained Large-Scale Language Model Guided by Audio-based Similar Caption Retrievalcited by 27
  9. 2021Investigating Local and Global Information for Automated Audio Captioning with Transfer Learningcited by 26
  10. 2020A Transformer-based Audio Captioning Model with Keyword Estimationcited by 24
  11. 2021An Encoder-Decoder Based Audio Captioning System With Transfer and Reinforcement Learningcited by 23
  12. 2021Automated Audio Captioning using Transfer Learning and Reconstruction Latent Space Similarity Regularizationcited by 12
1 more
  1. 2021Improving the Performance of Automated Audio Captioning via Integrating the Acoustic and Semantic Informationcited by 11

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