1 citations · 1 across the 5 of their papers we have counts for
5 papers
Few-Shot Generative Model Adaption via Identity Injection and Preservation
Yeqi He, Liang Li, Jiehua Zhang +4
Training generative models with limited data presents severe challenges of mode collapse. A common approach is to adapt a large pretrained generative model upon a target domain wit…
Progressive Homeostatic and Plastic Prompt Tuning for Audio-Visual Multi-Task Incremental Learning
Jiong Yin, Liang Li, Jiehua Zhang +3
Audio-visual multi-task incremental learning aims to continuously learn from multiple audio-visual tasks without the need for joint training on all tasks. The challenge of the prob…
Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation
Peihua Deng, Jiehua Zhang, Xichun Sheng +4
This paper explores the Class-Incremental Source-Free Unsupervised Domain Adaptation (CI-SFUDA) problem, where the unlabeled target data come incrementally without access to labele…
Generating High-quality Symbolic Music Using Fine-grained Discriminators
Zhedong Zhang, Liang Li, Jiehua Zhang +5
Existing symbolic music generation methods usually utilize discriminator to improve the quality of generated music via global perception of music. However, considering the complexi…
Progressive Depth Decoupling and Modulating for Flexible Depth Completion
Zhiwen Yang, Jiehua Zhang, Liang Li +3
Image-guided depth completion aims at generating a dense depth map from sparse LiDAR data and RGB image. Recent methods have shown promising performance by reformulating it as a cl…