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Found 18 papers, 8 papers with code
Authentic Emotion Mapping: Benchmarking Facial Expressions in Real News
In this paper, we present a novel benchmark for Emotion Recognition using facial landmarks extracted from realistic news videos.
Detecting and Restoring Non-Standard Hands in Stable Diffusion Generated Images
We introduce a pipeline to address anatomical inaccuracies in Stable Diffusion generated hand images.
Strengthening Skeletal Action Recognizers via Leveraging Temporal Patterns
Skeleton sequences are compact and lightweight.
ANUBIS: Skeleton Action Recognition Dataset, Review, and Benchmark
To this end, we present a review in the form of a taxonomy on existing works of skeleton-based action recognition.
Anonymization for Skeleton Action Recognition
Compared with RGB-video-based action recognition, skeleton-based action recognition is a safer way to protect the privacy of subjects while having competitive recognition performance.
Resolving Anomalies in the Behaviour of a Modularity Inducing Problem Domain with Distributional Fitness Evaluation
This dynamicity is imposed on top of an already complex fitness landscape.
Neural Network Classifier as Mutual Information Evaluator
We give a new view of neural network classifiers with softmax and cross-entropy as mutual information evaluators.
Position-Sensing Graph Neural Networks: Proactively Learning Nodes Relative Positions
Such GNNs are incapable of learning relative positions between graph nodes within a graph.
Disentangling Noise from Images: A Flow-Based Image Denoising Neural Network
The prevalent convolutional neural network (CNN) based image denoising methods extract features of images to restore the clean ground truth, achieving high denoising accuracy.
Fusing Higher-order Features in Graph Neural Networks for Skeleton-based Action Recognition
Recent skeleton-based action recognition methods extract features from 3D joint coordinates as spatial-temporal cues, using these representations in a graph neural network for feature fusion to boost recognition performance.
Ranked #20 on
Skeleton Based Action Recognition
on NTU RGB+D 120
Invertible Denoising Network: A Light Solution for Real Noise Removal
InvDN transforms the noisy input into a low-resolution clean image and a latent representation containing noise.
Are Deep Neural Architectures Losing Information? Invertibility Is Indispensable
Identifying the information lossless condition for deep neural architectures is important, because tasks such as image restoration require keep the detailed information of the input data as much as possible.
Rethinking Softmax with Cross-Entropy: Neural Network Classifier as Mutual Information Estimator
We show that optimising the parameters of classification neural networks with softmax cross-entropy is equivalent to maximising the mutual information between inputs and labels under the balanced data assumption.
Softmax Is Not an Artificial Trick: An Information-Theoretic View of Softmax in Neural Networks
Under this view, we can naturally and mathematically derive log-softmax as an inherent component in a neural network for evaluating the conditional mutual information between network output vectors and labels given an input datum.
Visual Saliency Maps Can Apply to Facial Expression Recognition
Human eyes concentrate different facial regions during distinct cognitive activities.
Facial Expression Recognition
Facial Expression Recognition (FER)
+2
Your Eyes Say You're Lying: An Eye Movement Pattern Analysis for Face Familiarity and Deceptive Cognition
Eye movement patterns reflect human latent internal cognitive activities.
Human-Computer Interaction
Why don't the modules dominate - Investigating the Structure of a Well-Known Modularity-Inducing Problem Domain
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.
