13 citations · 31 across the 55 of their papers we have counts for
8 papers · 1 filter
ESARM: 3D Emotional Speech-to-Animation via Reward Model from Automatically-Ranked Demonstrations
Xulong Zhang, Xiaoyang Qu, Haoxiang Shi +2
This paper proposes a novel 3D speech-to-animation (STA) generation framework designed to address the shortcomings of existing models in producing diverse and emotionally resonant…
Incremental Label Distribution Learning with Scalable Graph Convolutional Networks
Ziqi Jia, Xiaoyang Qu, Chenghao Liu +1
Label Distribution Learning (LDL) is an effective approach for handling label ambiguity, as it can analyze all labels at once and indicate the extent to which each label describes…
Task-agnostic Decision Transformer for Multi-type Agent Control with Federated Split Training
Zhiyuan Wang, Bokui Chen, Xiaoyang Qu +3
With the rapid advancements in artificial intelligence, the development of knowledgeable and personalized agents has become increasingly prevalent. However, the inherent variabilit…
PRENet: A Plane-Fit Redundancy Encoding Point Cloud Sequence Network for Real-Time 3D Action Recognition
Shenglin He, Xiaoyang Qu, Jiguang Wan +3
Recognizing human actions from point cloud sequence has attracted tremendous attention from both academia and industry due to its wide applications. However, most previous studies…
Retrieval-Augmented Audio Deepfake Detection
Zuheng Kang, Yayun He, Botao Zhao +4
With recent advances in speech synthesis including text-to-speech (TTS) and voice conversion (VC) systems enabling the generation of ultra-realistic audio deepfakes, there is growi…
Value-Driven Mixed-Precision Quantization for Patch-Based Inference on Microcontrollers
Wei Tao, Shenglin He, Kai Lu +5
Deploying neural networks on microcontroller units (MCUs) presents substantial challenges due to their constrained computation and memory resources. Previous researches have explor…
INCPrompt: Task-Aware incremental Prompting for Rehearsal-Free Class-incremental Learning
Zhiyuan Wang, Xiaoyang Qu, Jing Xiao +2
This paper introduces INCPrompt, an innovative continual learning solution that effectively addresses catastrophic forgetting. INCPrompt's key innovation lies in its use of adaptiv…
P2DT: Mitigating Forgetting in task-incremental Learning with progressive prompt Decision Transformer
Zhiyuan Wang, Xiaoyang Qu, Jing Xiao +2
Catastrophic forgetting poses a substantial challenge for managing intelligent agents controlled by a large model, causing performance degradation when these agents face new tasks.…