4 citations · 15 across the 14 of their papers we have counts for
8 papers · 1 filter
Unified Multi-View Orthonormal Non-Negative Graph Based Clustering Framework
Liangchen Liu, Qiuhong Ke, Chaojie Li +2
Spectral clustering is an effective methodology for unsupervised learning. Most traditional spectral clustering algorithms involve a separate two-step procedure and apply the trans…
DiffPose: Toward More Reliable 3D Pose Estimation
Jia Gong, Lin Geng Foo, Zhipeng Fan +3
Monocular 3D human pose estimation is quite challenging due to the inherent ambiguity and occlusion, which often lead to high uncertainty and indeterminacy. On the other hand, diff…
Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition
Anqi Zhu, Qiuhong Ke, Mingming Gong +1
Skeleton-based action recognition receives increasing attention because the skeleton representations reduce the amount of training data by eliminating visual information irrelevant…
Dynamic Spatio-Temporal Specialization Learning for Fine-Grained Action Recognition
Tianjiao Li, Lin Geng Foo, Qiuhong Ke +4
The goal of fine-grained action recognition is to successfully discriminate between action categories with subtle differences. To tackle this, we derive inspiration from the human…
IGFormer: Interaction Graph Transformer for Skeleton-based Human Interaction Recognition
Yunsheng Pang, Qiuhong Ke, Hossein Rahmani +2
Human interaction recognition is very important in many applications. One crucial cue in recognizing an interaction is the interactive body parts. In this work, we propose a novel…
ERA: Expert Retrieval and Assembly for Early Action Prediction
Lin Geng Foo, Tianjiao Li, Hossein Rahmani +2
Early action prediction aims to successfully predict the class label of an action before it is completely performed. This is a challenging task because the beginning stages of diff…
A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classification Tasks
Zihan Yang, Richard O. Sinnott, James Bailey +1
In recent years, one of the most popular techniques in the computer vision community has been the deep learning technique. As a data-driven technique, deep model requires enormous…
Progressive Video Summarization via Multimodal Self-supervised Learning
Li Haopeng, Ke Qiuhong, Gong Mingming +1
Modern video summarization methods are based on deep neural networks that require a large amount of annotated data for training. However, existing datasets for video summarization…