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Found 7 papers, 4 papers with code
Source-free Subject Adaptation for EEG-based Visual Recognition
In this paper, we introduce a novel and practical problem setup, namely source-free subject adaptation, where the source subject data are unavailable and only the pre-trained model parameters are provided for subject adaptation.
Fair Contrastive Learning for Facial Attribute Classification
Through extensive experiments on CelebA and UTK Face, we validate that the proposed method significantly outperforms SupCon and existing state-of-the-art methods in terms of the trade-off between top-1 accuracy and fairness.
Cut and Continuous Paste towards Real-time Deep Fall Detection
Deep learning based fall detection is one of the crucial tasks for intelligent video surveillance systems, which aims to detect unintentional falls of humans and alarm dangerous situations.
Inter-subject Contrastive Learning for Subject Adaptive EEG-based Visual Recognition
This paper tackles the problem of subject adaptive EEG-based visual recognition.
Subject Adaptive EEG-based Visual Recognition
It limits recognition systems to work only for the subjects involved in model training, which is undesirable for real-world scenarios where new subjects are frequently added.
