1 code implementation • 20 Aug 2024 • Jinghuai Jie, Yan Guo, Guixing Wu, Junmin Wu, Baojian Hua
On the one hand, EdgeNAT captures global contextual information and detailed local cues with DiNAT, on the other hand, it enhances feature representation with a novel SCAF-MLA decoder by utilizing both inter-spatial and inter-channel relationships of feature maps.
no code implementations • 26 May 2020 • XiangJi Wu, Ziwen Zhang, Jie Feng, Lei Zhou, Junmin Wu
We present an end-to-end trainable framework for P-frame compression in this paper.
1 code implementation • 8 Sep 2019 • Xia Liang, Junmin Wu, Jing Cao
In view of the above problem, this paper proposes a RNN-based Hierarchical Multi-modal Fusion Generation Variational Autoencoder (VAE) network, MIDI-Sandwich2, for multi-track symbolic music generation.
no code implementations • 2 Jul 2019 • Xia Liang, Junmin Wu, Yan Yin
The upper layer of HCVAE uses Global Variational Autoencoder(G-VAE) to analyze the latent vector sequence generated by the L-CVAE encoder, to explore the musical relationship between the bars, and to produce the song pieced together by multiple music bars generated by the L-CVAE decoder, which makes the song both have musical structure and sense of direction.