36 citations · 70 across the 10 of their papers we have counts for
12 papers
LightFuse: Relightable Interactive Gaussian Scene Reconstruction via Multi-Scan Fusion and 2D Gaussian Ray Tracing
Haonan Zhou, Gaoxiang Linghu, Youlin Jia +5
Relightable interactive scene reconstruction aims to build an editable 3D model from scans of different object arrangements and render new layouts under novel illumination. Existin…
Visual Anchoring in Diffusion: Multimodal Zero-Shot Skeleton Action Recognition
Zehao Bao, Shujun Guo, Bruce X. B. Yu
Zero-shot Skeleton Action Recognition (ZSAR) remains ambiguous when unseen actions share similar skeleton joint dynamics but differ in objects or scene context. RGB provides these…
Who Decides How Knowing Becomes Doing? Redistributing Authority in Human-AI Music Co-Creation
Zhejing Hu, Yan Liu, Zhi Zhang +3
In the era of human-AI co-creation, the maxim "knowing is easy, doing is hard" is redefined. AI has the potential to ease execution, yet the essence of "hard" lies in who governs t…
MusicScaffold: Bridging Machine Efficiency and Human Growth in Adolescent Creative Education through Generative AI
Zhejing Hu, Yan Liu, Zhi Zhang +4
Adolescence is marked by strong creative impulses but limited strategies for structured expression, often leading to frustration or disengagement. While generative AI lowers techni…
CompLex: Music Theory Lexicon Constructed by Autonomous Agents for Automatic Music Generation
Zhejing Hu, Yan Liu, Gong Chen +1
Generative artificial intelligence in music has made significant strides, yet it still falls short of the substantial achievements seen in natural language processing, primarily du…
PTSM: Physiology-aware and Task-invariant Spatio-temporal Modeling for Cross-Subject EEG Decoding
Changhong Jing, Yan Liu, Shuqiang Wang +5
Cross-subject electroencephalography (EEG) decoding remains a fundamental challenge in brain-computer interface (BCI) research due to substantial inter-subject variability and the…
GLA-GCN: Global-local Adaptive Graph Convolutional Network for 3D Human Pose Estimation from Monocular Video
Bruce X. B. Yu, Zhi Zhang, Yongxu Liu +3
3D human pose estimation has been researched for decades with promising fruits. 3D human pose lifting is one of the promising research directions toward the task where both estimat…
Visual Tuning
Bruce X. B. Yu, Jianlong Chang, Haixin Wang +9
Fine-tuning visual models has been widely shown promising performance on many downstream visual tasks. With the surprising development of pre-trained visual foundation models, visu…
Towards a Unified View on Visual Parameter-Efficient Transfer Learning
Bruce X. B. Yu, Jianlong Chang, Lingbo Liu +2
Parameter efficient transfer learning (PETL) aims at making good use of the representation knowledge in the pre-trained large models by fine-tuning a small number of parameters. Re…
Prompt-Matched Semantic Segmentation
Lingbo Liu, Jianlong Chang, Bruce X. B. Yu +3
The objective of this work is to explore how to effectively and efficiently adapt pre-trained visual foundation models to various downstream tasks of semantic segmentation. Previou…
Skeleton Focused Human Activity Recognition in RGB Video
Bruce X. B. Yu, Yan Liu, Keith C. C. Chan
The data-driven approach that learns an optimal representation of vision features like skeleton frames or RGB videos is currently a dominant paradigm for activity recognition. Whil…
Effective Human Activity Recognition Based on Small Datasets
Bruce X. B. Yu, Yan Liu, Keith C. C. Chan
Most recent work on vision-based human activity recognition (HAR) focuses on designing complex deep learning models for the task. In so doing, there is a requirement for large data…