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20172026
most citedDeep generative-contrastive networks for facial expression recognition

75 citations · 80 across the 10 of their papers we have counts for

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7 papers · 1 filter

cs.CV2023

1st Place in ICCV 2023 Workshop Challenge Track 1 on Resource Efficient Deep Learning for Computer Vision: Budgeted Model Training Challenge

Youngjun Kwak, Seonghun Jeong, Yunseung Lee +1

The budgeted model training challenge aims to train an efficient classification model under resource limitations. To tackle this task in ImageNet-100, we describe a simple yet effe…

cs.CV2023

ProtoFL: Unsupervised Federated Learning via Prototypical Distillation

Hansol Kim, Youngjun Kwak, Minyoung Jung +3

Federated learning (FL) is a promising approach for enhancing data privacy preservation, particularly for authentication systems. However, limited round communications, scarce repr…

cs.CV2023

Robust face anti-spoofing framework with Convolutional Vision Transformer

Yunseung Lee, Youngjun Kwak, Jinho Shin

Owing to the advances in image processing technology and large-scale datasets, companies have implemented facial authentication processes, thereby stimulating increased focus on fa…

cs.CV2023

Liveness score-based regression neural networks for face anti-spoofing

Youngjun Kwak, Minyoung Jung, Hunjae Yoo +2

Previous anti-spoofing methods have used either pseudo maps or user-defined labels, and the performance of each approach depends on the accuracy of the third party networks generat…

cs.CV2019★ 4 cited

A Generalized and Robust Method Towards Practical Gaze Estimation on Smart Phone

Tianchu Guo, Yongchao Liu, Hui Zhang +5

Gaze estimation for ordinary smart phone, e.g. estimating where the user is looking at on the phone screen, can be applied in various applications. However, the widely used appeara…

cs.CV2018★ 1 cited

Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss

Sangil Jung, Changyong Son, Seohyung Lee +5

Reducing bit-widths of activations and weights of deep networks makes it efficient to compute and store them in memory, which is crucial in their deployments to resource-limited de…

cs.CV2017★ 75 cited

Deep generative-contrastive networks for facial expression recognition

Youngsung Kim, ByungIn Yoo, Youngjun Kwak +2

As the expressive depth of an emotional face differs with individuals or expressions, recognizing an expression using a single facial image at a moment is difficult. A relative exp…