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Found 8 papers, 1 papers with code
Multi-Scale Wavelet Transformer for Face Forgery Detection
To overcome these limitations, we propose a multi-scale wavelet transformer framework for face forgery detection.
Weakly Supervised Regional and Temporal Learning for Facial Action Unit Recognition
Automatic facial action unit (AU) recognition is a challenging task due to the scarcity of manual annotations.
Few-shot One-class Domain Adaptation Based on Frequency for Iris Presentation Attack Detection
We thus define a new domain adaptation setting called Few-shot One-class Domain Adaptation (FODA), where adaptation only relies on a limited number of target bonafide samples.
Unimodal-Concentrated Loss: Fully Adaptive Label Distribution Learning for Ordinal Regression
In this paper, we emphatically summarize that learning an adaptive label distribution on ordinal regression tasks should follow three principles.
Learning Multiple Explainable and Generalizable Cues for Face Anti-spoofing
However, many other generalizable cues are unexplored for face anti-spoofing, which limits their performance under cross-dataset testing.
Self-Supervised Regional and Temporal Auxiliary Tasks for Facial Action Unit Recognition
Based on these two self-supervised auxiliary tasks, local features, mutual relation and motion cues of AUs are better captured in the backbone network with the proposed regional and temporal based auxiliary task learning (RTATL) framework.
Self-Domain Adaptation for Face Anti-Spoofing
In this paper, we propose a self-domain adaptation framework to leverage the unlabeled test domain data at inference.
Multi-Level Adaptive Region of Interest and Graph Learning for Facial Action Unit Recognition
In order to incorporate the intra-level AU relation and inter-level AU regional relevance simultaneously, a multi-level AU relation graph is constructed and graph convolution is performed to further enhance AU regional features of each level.
