12 citations · 12 across the 10 of their papers we have counts for
9 papers · 1 filter
ActionArt: Advancing Multimodal Large Models for Fine-Grained Human-Centric Video Understanding
Yi-Xing Peng, Qize Yang, Yu-Ming Tang +4
Fine-grained understanding of human actions and poses in videos is essential for human-centric AI applications. In this work, we introduce ActionArt, a fine-grained video-caption d…
Modeling Multiple Normal Action Representations for Error Detection in Procedural Tasks
Wei-Jin Huang, Yuan-Ming Li, Zhi-Wei Xia +4
Error detection in procedural activities is essential for consistent and correct outcomes in AR-assisted and robotic systems. Existing methods often focus on temporal ordering erro…
ChainHOI: Joint-based Kinematic Chain Modeling for Human-Object Interaction Generation
Ling-An Zeng, Guohong Huang, Yi-Lin Wei +4
We propose ChainHOI, a novel approach for text-driven human-object interaction (HOI) generation that explicitly models interactions at both the joint and kinetic chain levels. Unli…
MaintaAvatar: A Maintainable Avatar Based on Neural Radiance Fields by Continual Learning
Shengbo Gu, Yu-Kun Qiu, Yu-Ming Tang +2
The generation of a virtual digital avatar is a crucial research topic in the field of computer vision. Many existing works utilize Neural Radiance Fields (NeRF) to address this is…
TechCoach: Towards Technical-Point-Aware Descriptive Action Coaching
Yuan-Ming Li, An-Lan Wang, Kun-Yu Lin +4
To guide a learner in mastering action skills, it is crucial for a coach to 1) reason through the learner's action execution and technical points (TechPoints), and 2) provide detai…
Rethinking Few-shot Class-incremental Learning: Learning from Yourself
Yu-Ming Tang, Yi-Xing Peng, Jingke Meng +1
Few-shot class-incremental learning (FSCIL) aims to learn sequential classes with limited samples in a few-shot fashion. Inherited from the classical class-incremental learning set…
XOV-Action: Towards Generalizable Open-Vocabulary Action Recognition
Kun-Yu Lin, Henghui Ding, Jia-Run Du +6
Inspired by the impressive success of image-text foundation models, recent works have proposed to adapt these foundation models to video data, leading to efficient and effective vi…
When Prompt-based Incremental Learning Does Not Meet Strong Pretraining
Yu-Ming Tang, Yi-Xing Peng, Wei-Shi Zheng
Incremental learning aims to overcome catastrophic forgetting when learning deep networks from sequential tasks. With impressive learning efficiency and performance, prompt-based m…
DilateFormer: Multi-Scale Dilated Transformer for Visual Recognition
Jiayu Jiao, Yu-Ming Tang, Kun-Yu Lin +4
As a de facto solution, the vanilla Vision Transformers (ViTs) are encouraged to model long-range dependencies between arbitrary image patches while the global attended receptive f…