Learning visual representation of high quality is essential for image classification. Recently, a series of contrastive representation learning methods have achieved preeminent success. Particularly, SupCon [18] outperformed the dominant methods based on cross-entropy loss in representation learning. However, we notice that there could be potential ethical risks in supervised contrastive learning. In this paper, we for the first time analyze unfairness caused by supervised contrastive learning and propose a new Fair Supervised Contrastive Loss (FSCL) for fair visual representation learning. Inheriting the philosophy of supervised contrastive learning, it encourages representation of the same class to be closer to each other than that of different classes, while ensuring fairness by penalizing the inclusion of sensitive attribute information in representation. In addition, we introduce a group-wise normalization to diminish the disparities of intra-group compactness and inter-class separability between demographic groups that arouse unfair 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-l accuracy and fairness. Moreover, our method is robust to the intensity of data bias and effectively works in incomplete supervised settings. Our code is available at https://github.com/sungho-CoolG/FSCL.

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. In this work, we propose a simple and efficient framework to detect falls through a single and small-sized convolutional neural network. To this end, we first introduce a new image synthesis method that represents human motion in a single frame. This simplifies the fall detection task as an image classification task. Besides, the proposed synthetic data generation method enables to generate a sufficient amount of training dataset, resulting in satisfactory performance even with the small model. At the inference step, we also represent real human motion in a single image by estimating mean of input frames. In the experiment, we conduct both qualitative and quantitative evaluations on URFD and AIHub airport datasets to show the effectiveness of our method.

Although AI systems archive a great success in various societal fields, there still exists a challengeable issue of outputting discriminatory results with respect to protected attributes (e.g., gender and age). The popular approach to solving the issue is to remove protected attribute information in the decision process. However, this approach has a limitation that beneficial information for target tasks may also be eliminated. To overcome the limitation, we propose Fairness-aware Disentangling Variational Auto-Encoder (FD-VAE) that disentangles data representation into three subspaces: 1) Target Attribute Latent (TAL), 2) Protected Attribute Latent (PAL), 3) Mutual Attribute Latent (MAL). On top of that, we propose a decorrelation loss that aligns the overall information into each subspace, instead of removing the protected attribute information. After learning the representation, we re-encode MAL to include only target information and combine it with TAL to perform downstream tasks. In our experiments on CelebA and UTK Face datasets, we show that the proposed method mitigates unfairness in facial attribute classification tasks with respect to gender and age. Ours outperforms previous methods by large margins on two standard fairness metrics, equal opportunity and equalized odds.

With great research advances on Brain-Computer-Interface (BCI) systems, Electroencephalography (EEG) based driver fatigue state classification models have shown its effectiveness. However, EEG signals contain large differences between individuals, making it hard to build a unified model among individuals. In this paper, we propose a subject- independent EEG-based driver fatigue state (i.e., awake, tired, and drowsy) classification model that mitigates a performance gap between subjects. To this end, we exploit an adversarial training strategy to make our classification model misclassify the subject labels. Besides, we propose an Intersubject Feature Distance Minimization (IFDM) method that minimizes the Wasserstein distance between two different subject groups of the same class to reduce the individual performance discrepancy. Our method is also designed to enable training even if the subject labels are not sufficiently included in the EEG dataset. To demonstrate the ability of the proposed method, we conduct a drowsiness classification task on a publicly available SEED-VIG dataset. The experimental results show our model achieves the highest accuracy and the lowest individual performance variability.

Fairness becomes a critical issue of computer vision to reduce discriminative factors in various systems. Among computer vision tasks, Image-to-Image translation for facial attributes editing can yield discriminative results. The unexpected gender changed results can be generated instead of editing target attributes due to the dataset imbalance problem. In this work, we propose a framework of unsupervised Image-to-Image translation that learns a fair representation by separating the latent space of our model into two purposes: 1) Target Attribute Editing, 2) Gender Preserving. We evaluate the proposed framework on CelebA dataset. Both quantitive and qualitative results demonstrate that our method improves image quality and fairness than the prior Image-to-Image translation method.