4 citations · 15 across the 14 of their papers we have counts for
15 papers
Answering from Sure to Uncertain: Uncertainty-Aware Curriculum Learning for Video Question Answering
Haopeng Li, Mohammed Bennamoun, Jun Liu +2
While significant advancements have been made in video question answering (VideoQA), the potential benefits of enhancing model generalization through tailored difficulty scheduling…
Sports-QA: A Large-Scale Video Question Answering Benchmark for Complex and Professional Sports
Haopeng Li, Andong Deng, Jun Liu +5
Reasoning over sports videos for question answering is an important task with numerous applications, such as player training and information retrieval. However, this task has not b…
Unsupervised Domain Adaptation via Domain-Adaptive Diffusion
Duo Peng, Qiuhong Ke, Yinjie Lei +1
Unsupervised Domain Adaptation (UDA) is quite challenging due to the large distribution discrepancy between the source domain and the target domain. Inspired by diffusion models wh…
Diffusion-based Image Translation with Label Guidance for Domain Adaptive Semantic Segmentation
Duo Peng, Ping Hu, Qiuhong Ke +1
Translating images from a source domain to a target domain for learning target models is one of the most common strategies in domain adaptive semantic segmentation (DASS). However,…
GradMDM: Adversarial Attack on Dynamic Networks
Jianhong Pan, Lin Geng Foo, Qichen Zheng +4
Dynamic neural networks can greatly reduce computation redundancy without compromising accuracy by adapting their structures based on the input. In this paper, we explore the robus…
Progressive Channel-Shrinking Network
Jianhong Pan, Siyuan Yang, Lin Geng Foo +4
Currently, salience-based channel pruning makes continuous breakthroughs in network compression. In the realization, the salience mechanism is used as a metric of channel salience…
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…
Precondition and Effect Reasoning for Action Recognition
Yoo Hongsang, Li Haopeng, Ke Qiuhong +2
Human action recognition has drawn a lot of attention in the recent years due to the research and application significance. Most existing works on action recognition focus on learn…