Synth-AC: Enhancing Audio Captioning with Synthetic Supervision

G. C. Tomas Mikolov, Kai Chen, “Efficient estimation of word representations in vector space,” in Proc. Int. Conf. Learn. Represent. , 2013

2013

Earlier work this paper cites.

T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft COCO: Common objects in context,” in Proc. European Conf. Comput. Vision , 2014, pp. 740–755

2014

Earlier work this paper cites.

R. Vedantam, C. Lawrence Zitnick, and D. Parikh, “CIDEr: Consensus-based image description evaluation,” in Proc. IEEE Comput. Soc. Conf. Comput. Vision Pattern Recognit. , 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 Proc. European Conf. Comput. Vision , 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 Proc. IEEE Workshop Appl. Signal Process. Audio Acoust. , 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 Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. , 2017, pp. 776–780

2017

Earlier work this paper cites.

I. Loshchilov and F. Hutter, “Fixing weight decay regularization in adam. arxiv 2017,” arXiv preprint arXiv:1711.05101 , vol. 7, 2017

Original

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 Proc. IEEE Int. Conf. Comput. Vision , 2017, pp. 873–881

2017

Earlier work this paper cites.

T. Baltrušaitis, C. Ahuja, and L.-P. Morency, “Multimodal machine learning: A survey and taxonomy,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 41, no. 2, pp. 423–443, 2018

2018

Cited alongside, same era.

C. D. Kim, B. Kim, H. Lee, and G. Kim, “AudioCaps: Generating captions for audios in the wild,” in Proc. N. Am. Chapter Assoc. Comput. Linguistics: Hum. Lang. Technol. , 2019, pp. 119–132

2019

Cited alongside, same era.

K. Drossos, S. Lipping, and T. Virtanen, “Clotho: An audio captioning dataset,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. , 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 Trans. Audio Speech Lang. , vol. 28, pp. 2880–2894, 2020

2020

Cited alongside, same era.

X. Liu, X. Mei, Q. Huang, J. Sun, J. Zhao, H. Liu, M. D. Plumbley, V. Kilic, and W. Wang, “Leveraging pre-trained BERT for audio captioning,” in Proc. European Signal Proces. Conf. , 2022, pp. 1145–1149

2022

Later among the works it cites.

F. Xiao, J. Guan, H. Lan, Q. Zhu, and W. Wang, “Local information assisted attention-free decoder for audio captioning,” IEEE Signal Process. Lett. , vol. 29, pp. 1604–1608, 2022

2022

Later among the works it cites.

Z. Zhou, Z. Zhang, X. Xu, Z. Xie, M. Wu, and K. Q. Zhu, “Can audio captions be evaluated with image caption metrics?” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. , 2022, pp. 981–985

2022

Later among the works it cites.

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

Closest in time.