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1st Place in ICCV 2023 Workshop Challenge Track 1 on Resource Efficient Deep Learning for Computer Vision: Budgeted Model Training Challenge
The budgeted model training challenge aims to train an efficient classification model under resource limitations.
Robust face anti-spoofing framework with Convolutional Vision Transformer
Owing to the advances in image processing technology and large-scale datasets, companies have implemented facial authentication processes, thereby stimulating increased focus on face anti-spoofing (FAS) against realistic presentation attacks.
ProtoFL: Unsupervised Federated Learning via Prototypical Distillation
Federated learning (FL) is a promising approach for enhancing data privacy preservation, particularly for authentication systems.
Liveness score-based regression neural networks for face anti-spoofing
In this paper, we propose a liveness score-based regression network for overcoming the dependency on third party networks and users.
A Generalized and Robust Method Towards Practical Gaze Estimation on Smart Phone
For the second issue, we define a new metric to measure the robustness of gaze estimator, and propose an adversarial training based Disturbance with Ordinal loss (DwO) method to improve it.
Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss
We demonstrate the effectiveness of our trainable quantizer on ImageNet dataset with various network architectures such as ResNet-18, -34 and AlexNet, on which it outperforms existing methods to achieve the state-of-the-art accuracy.
Deep generative-contrastive networks for facial expression recognition
In this paper, we propose to utilize contrastive representation that embeds a distinctive expressive factor for a discriminative purpose.
Facial Expression Recognition
Facial Expression Recognition (FER)
