Zero-Shot Audio Captioning Using Soft and Hard Prompts

K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, “BLEU: a method for automatic evaluation of machine translation,” in Proceedings of the 40th annual meeting of the Association for Computational Linguistics , 2002, pp. 311–318

2002

Earlier work this paper cites.

C.-Y. Lin, “ROUGE: A package for automatic evaluation of summaries,” in Text summarization branches out , 2004, pp. 74–81

2004

Earlier work this paper cites.

S. Banerjee and A. Lavie, “METEOR: An automatic metric for mt evaluation with improved correlation with human judgments,” in Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization , 2005, pp. 65–72

2005

Earlier work this paper cites.

X. Chen, H. Fang, T.-Y. Lin, R. Vedantam, S. Gupta, P. Dollár, and C. L. Zitnick, “Microsoft COCO Captions: Data collection and evaluation server,” arXiv preprint arXiv:1504.00325 , 2015

Original

2015

Earlier work this paper cites.

R. Vedantam, C. Lawrence Zitnick, and D. Parikh, “CIDER: Consensus-based image description evaluation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 4566–4575

2015

Earlier work this paper cites.

P. Anderson, B. Fernando, M. Johnson, and S. Gould, “SPICE: Semantic propositional image caption evaluation,” in European conference on computer vision . Springer, 2016, pp. 382–398

2016

Earlier work this paper cites.

K. Drossos, S. Adavanne, and T. Virtanen, “Automated audio captioning with recurrent neural networks,” in 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) , 2017, pp. 374–378

2017

Earlier work this paper cites.

J. F. Gemmeke, D. P. Ellis, D. Freedman, A. Jansen, W. Lawrence, R. C. Moore, M. Plakal, and M. Ritter, “AudioSet: An ontology and human-labeled dataset for audio events,” in 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP) . IEEE, 2017, pp. 776–780

2017

Earlier work this paper cites.

S. Liu, Z. Zhu, N. Ye, S. Guadarrama, and K. Murphy, “Improved image captioning via policy gradient optimization of spider,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 873–881

2017

Earlier work this paper cites.

J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805 , 2018

Original

2018

Earlier work this paper cites.

A. v. d. Oord, Y. Li, and O. Vinyals, “Representation learning with contrastive predictive coding,” arXiv preprint arXiv:1807.03748 , 2018

Original

2018

Earlier work this paper cites.

C. D. Kim, B. Kim, H. Lee, and G. Kim, “AudioCaps: Generating captions for audios in the wild,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) , 2019, pp. 119–132

2019

Earlier work this paper cites.

S. Lipping, K. Drossos, and T. Virtanen, “Crowdsourcing a dataset of audio captions,” in Acoustic Scenes and Events 2019 Workshop (DCASE2019) , 2019, p. 139

2019

Earlier work this paper cites.

Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov, “ROBERTA: A robustly optimized bert pretraining approach,” arXiv preprint arXiv:1907.11692 , 2019

Original

2019

Earlier work this paper cites.

A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019

2019

Cited alongside, same era.

K. Drossos, S. Lipping, and T. Virtanen, “Clotho: An audio captioning dataset,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2020, pp. 736–740

2020

Cited alongside, same era.

Q. Kong, Y. Cao, T. Iqbal, Y. Wang, W. Wang, and M. D. Plumbley, “PANNs: Large-scale pretrained audio neural networks for audio pattern recognition,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 28, pp. 2880–2894, 2020

2020

Cited alongside, same era.

I. M. Morato and A. Mesaros, “Diversity and bias in audio captioning datasets,” in Detection and Classication of Acoustic Scenes and Events , 2021, pp. 90–94

2021

Cited alongside, same era.

X. Mei, C. Meng, H. Liu, Q. Kong, T. Ko, C. Zhao, M. D. Plumbley, Y. Zou, and W. Wang, “WavCaps: A chatgpt-assisted weakly-labelled audio captioning dataset for audio-language multimodal research,” arXiv preprint arXiv:2303.17395 , 2023

Original

2023

Later among the works it cites.

B. Elizalde, S. Deshmukh, M. A. Ismail, and H. Wang, “Clap learning audio concepts from natural language supervision,” in ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2023, pp. 1–5

2023

Later among the works it cites.

M. Kim, K. Sung-Bin, and T.-H. Oh, “Prefix tuning for automated audio captioning,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2023

2023

Later among the works it cites.

T. Shaharabany, A. Shaulov, and L. Wolf, “Zero-shot audio captioning via audibility guidance,” arXiv preprint arXiv:2309.03884 , 2023

Original

2023

Later among the works it cites.