36 citations · 85 across the 18 of their papers we have counts for
18 papers
Authentic Emotion Mapping: Benchmarking Facial Expressions in Real News
Qixuan Zhang, Zhifeng Wang, Yang Liu +4
In this paper, we present a novel benchmark for Emotion Recognition using facial landmarks extracted from realistic news videos. Traditional methods relying on RGB images are resou…
Strengthening Skeletal Action Recognizers via Leveraging Temporal Patterns
Zhenyue Qin, Pan Ji, Dongwoo Kim +3
Skeleton sequences are compact and lightweight. Numerous skeleton-based action recognizers have been proposed to classify human behaviors. In this work, we aim to incorporate compo…
Representation-Centric Survey of Supervised Skeletal Action Recognition and the New Benchmark
Yang Liu, Jiyao Yang, Madhawa Perera +8
3D skeletal action recognition has emerged as a powerful alternative to traditional RGB and depth-based approaches, offering robustness to environmental variations, computational e…
Anonymization for Skeleton Action Recognition
Saemi Moon, Myeonghyeon Kim, Zhenyue Qin +2
Skeleton-based action recognition attracts practitioners and researchers due to the lightweight, compact nature of datasets. Compared with RGB-video-based action recognition, skele…
Resolving Anomalies in the Behaviour of a Modularity Inducing Problem Domain with Distributional Fitness Evaluation
Zhenyue Qin, Tom Gedeon, Bob McKay
Discrete gene regulatory networks (GRNs) play a vital role in the study of robustness and modularity. A common method of evaluating the robustness of GRNs is to measure their abili…
Informative Class Activation Maps
Zhenyue Qin, Dongwoo Kim, Tom Gedeon
We study how to evaluate the quantitative information content of a region within an image for a particular label. To this end, we bridge class activation maps with information theo…
Neural Network Classifier as Mutual Information Evaluator
Zhenyue Qin, Dongwoo Kim, Tom Gedeon
Cross-entropy loss with softmax output is a standard choice to train neural network classifiers. We give a new view of neural network classifiers with softmax and cross-entropy as…
Disentangling Noise from Images: A Flow-Based Image Denoising Neural Network
Yang Liu, Saeed Anwar, Zhenyue Qin +3
The prevalent convolutional neural network (CNN) based image denoising methods extract features of images to restore the clean ground truth, achieving high denoising accuracy. Howe…
Position-Sensing Graph Neural Networks: Proactively Learning Nodes Relative Positions
Zhenyue Qin, Yiqun Zhang Saeed Anwar, Dongwoo Kim +3
Most existing graph neural networks (GNNs) learn node embeddings using the framework of message passing and aggregation. Such GNNs are incapable of learning relative positions betw…
Fusing Higher-order Features in Graph Neural Networks for Skeleton-based Action Recognition
Zhenyue Qin, Yang Liu, Pan Ji +5
Skeleton sequences are lightweight and compact, and thus are ideal candidates for action recognition on edge devices. Recent skeleton-based action recognition methods extract featu…
Invertible Denoising Network: A Light Solution for Real Noise Removal
Yang Liu, Zhenyue Qin, Saeed Anwar +4
Invertible networks have various benefits for image denoising since they are lightweight, information-lossless, and memory-saving during back-propagation. However, applying inverti…
Are Deep Neural Architectures Losing Information? Invertibility Is Indispensable
Yang Liu, Zhenyue Qin, Saeed Anwar +2
Ever since the advent of AlexNet, designing novel deep neural architectures for different tasks has consistently been a productive research direction. Despite the exceptional perfo…
Rethinking Softmax with Cross-Entropy: Neural Network Classifier as Mutual Information Estimator
Zhenyue Qin, Dongwoo Kim, Tom Gedeon
Mutual information is widely applied to learn latent representations of observations, whilst its implication in classification neural networks remain to be better explained. We sho…
Softmax Is Not an Artificial Trick: An Information-Theoretic View of Softmax in Neural Networks
Zhenyue Qin, Dongwoo Kim
Despite great popularity of applying softmax to map the non-normalised outputs of a neural network to a probability distribution over predicting classes, this normalised exponentia…
Visual Saliency Maps Can Apply to Facial Expression Recognition
Zhenyue Qin, Jie Wu
Human eyes concentrate different facial regions during distinct cognitive activities. We study utilising facial visual saliency maps to classify different facial expressions into d…
Your Eyes Say You're Lying: An Eye Movement Pattern Analysis for Face Familiarity and Deceptive Cognition
Jiaxu Zuo, Tom Gedeon, Zhenyue Qin
Eye movement patterns reflect human latent internal cognitive activities. We aim to discover eye movement patterns during face recognition under different cognitions of information…
Neural Networks Assist Crowd Predictions in Discerning the Veracity of Emotional Expressions
Zhenyue Qin, Tom Gedeon, Sabrina Caldwell
Crowd predictions have demonstrated powerful performance in predicting future events. We aim to understand crowd prediction efficacy in ascertaining the veracity of human emotional…
Why don't the modules dominate - Investigating the Structure of a Well-Known Modularity-Inducing Problem Domain
Zhenyue Qin, Robert McKay, Tom Gedeon
Wagner's modularity inducing problem domain is a key contribution to the study of the evolution of modularity, including both evolutionary theory and evolutionary computation. We s…