activity
20172020
most citedDeep Cross-Modal Correlation Learning for Audio and Lyrics in Music Retrieval

15 citations · 22 across the 4 of their papers we have counts for

collaborators

7 papers

cs.AI20203 cited

Conditional Hybrid GAN for Sequence Generation

Yi Yu, Abhishek Srivastava, Rajiv Ratn Shah

Conditional sequence generation aims to instruct the generation procedure by conditioning the model with additional context information, which is a self-supervised learning issue (…

cs.CV20203 cited

PAI-BPR: Personalized Outfit Recommendation Scheme with Attribute-wise Interpretability

Dikshant Sagar, Jatin Garg, Prarthana Kansal +3

Fashion is an important part of human experience. Events such as interviews, meetings, marriages, etc. are often based on clothing styles. The rise in the fashion industry and its…

eess.AS20201 cited

Unsupervised Generative Adversarial Alignment Representation for Sheet music, Audio and Lyrics

Donghuo Zeng, Yi Yu, Keizo Oyama

Sheet music, audio, and lyrics are three main modalities during writing a song. In this paper, we propose an unsupervised generative adversarial alignment representation (UGAAR) mo…

cs.CL2020

End-to-end Named Entity Recognition from English Speech

Hemant Yadav, Sreyan Ghosh, Yi Yu +1

Named entity recognition (NER) from text has been a widely studied problem and usually extracts semantic information from text. Until now, NER from speech is mostly studied in a tw…

cs.LG2019

Text2FaceGAN: Face Generation from Fine Grained Textual Descriptions

Osaid Rehman Nasir, Shailesh Kumar Jha, Manraj Singh Grover +3

Powerful generative adversarial networks (GAN) have been developed to automatically synthesize realistic images from text. However, most existing tasks are limited to generating si…

cs.MM2019

Audio-Visual Embedding for Cross-Modal MusicVideo Retrieval through Supervised Deep CCA

Donghuo Zeng, Yi Yu, Keizo Oyama

Deep learning has successfully shown excellent performance in learning joint representations between different data modalities. Unfortunately, little research focuses on cross-moda…