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Found 11 papers, 4 papers with code
Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild
Past research on facial expressions have used relatively limited datasets, which makes it unclear whether current methods can be employed in real world.
Deep Facial Expression Recognition: A Survey
We then introduce the available datasets that are widely used in the literature and provide accepted data selection and evaluation principles for these datasets.
Facial Expression Recognition
Facial Expression Recognition (FER)
A Deeper Look at Facial Expression Dataset Bias
Datasets play an important role in the progress of facial expression recognition algorithms, but they may suffer from obvious biases caused by different cultures and collection conditions.
Facial Expression Recognition
Facial Expression Recognition (FER)
Learning to Ask Screening Questions for Job Postings
A critical aspect of this goal is matching jobs with qualified applicants.
Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors
Although the quest for more accurate solutions is pushing deep learning research towards larger and more complex algorithms, edge devices demand efficient inference and therefore reduction in model size, latency and energy consumption.
RAF-AU Database: In-the-Wild Facial Expressions with Subjective Emotion Judgement and Objective AU Annotations
Much of the work on automatic facial expression recognition relies on databases containing a certain number of emotion classes and their exaggerated facial configurations (generally six prototypical facial expressions), based on Ekman's Basic Emotion Theory.
Facial Expression Recognition
Facial Expression Recognition (FER)
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Edge Inference with Fully Differentiable Quantized Mixed Precision Neural Networks
Quantizing the parameters and operations to lower bit-precision offers substantial memory and energy savings for neural network inference, facilitating the use of DNNs on edge computing platforms.
LSAP: Rethinking Inversion Fidelity, Perception and Editability in GAN Latent Space
In this work, we first point out that these two characteristics are related to the degree of alignment (or disalignment) of the inverse codes with the synthetic distribution.
Deep Learning Technique for Human Parsing: A Survey and Outlook
Human parsing aims to partition humans in image or video into multiple pixel-level semantic parts.
FairSample: Training Fair and Accurate Graph Convolutional Neural Networks Efficiently
To address the bias in node features and model parameters, FairSample is complemented by a regularization objective to optimize fairness.
CloudFort: Enhancing Robustness of 3D Point Cloud Classification Against Backdoor Attacks via Spatial Partitioning and Ensemble Prediction
In this paper, we propose CloudFort, a novel defense mechanism designed to enhance the robustness of 3D point cloud classifiers against backdoor attacks.
