activity
20202023
most citedEfficient Multi-Scale Attention Module with Cross-Spatial Learning

1.7k citations · 1.7k across the 6 of their papers we have counts for

collaborators

6 papers

cs.SD2023★ 1 cited

MelodyGLM: Multi-task Pre-training for Symbolic Melody Generation

Xinda Wu, Zhijie Huang, Kejun Zhang +5

Pre-trained language models have achieved impressive results in various music understanding and generation tasks. However, existing pre-training methods for symbolic melody generat…

cs.CV2023★ 1.7k cited

Efficient Multi-Scale Attention Module with Cross-Spatial Learning

Daliang Ouyang, Su He, Guozhong Zhang +4

Remarkable effectiveness of the channel or spatial attention mechanisms for producing more discernible feature representation are illustrated in various computer vision tasks. Howe…

cs.CV2023

ACE: Zero-Shot Image to Image Translation via Pretrained Auto-Contrastive-Encoder

Sihan Xu, Zelong Jiang, Ruisi Liu +2

Image-to-image translation is a fundamental task in computer vision. It transforms images from one domain to images in another domain so that they have particular domain-specific c…

cs.SD2023★ 3 cited

WuYun: Exploring hierarchical skeleton-guided melody generation using knowledge-enhanced deep learning

Kejun Zhang, Xinda Wu, Tieyao Zhang +5

Although deep learning has revolutionized music generation, existing methods for structured melody generation follow an end-to-end left-to-right note-by-note generative paradigm an…

cs.CY2021★ 6 cited

Enhancing Knowledge Tracing via Adversarial Training

Xiaopeng Guo, Zhijie Huang, Jie Gao +3

We study the problem of knowledge tracing (KT) where the goal is to trace the students' knowledge mastery over time so as to make predictions on their future performance. Owing to…

cs.MM2020

An Efficient QP Variable Convolutional Neural Network Based In-loop Filter for Intra Coding

Zhijie Huang, Xiaopeng Guo, Mingyu Shang +2

In this paper, a novel QP variable convolutional neural network based in-loop filter is proposed for VVC intra coding. To avoid training and deploying multiple networks, we develop…