In this paper, we address the issue of producing cancelable biometric templates; a necessary
feature in the deployment of any biometric authentication system. We propose a novel …

Large-scale object detection datasets (eg, MS-COCO) try to define the ground truth bounding
boxes as clear as possible. However, we observe that ambiguities are still introduced …

We propose local binary convolution (LBC), an efficient alternative to convolutional layers in
standard convolutional neural networks (CNN). The design principles of LBC are motivated …

Biometrics has gained a lot of attention over recent years as a way to identify individuals. Of
all biometrics-based techniques, the iris-pattern-based systems have recently shown very …

We motivate and present feature selective anchor-free (FSAF) module, a simple and effective
building block for single-shot object detectors. It can be plugged into single-shot detectors …

Pseudo labeling and consistency regularization approaches with confidence-based
thresholding have made great progress in semi-supervised learning (SSL). In this paper, we …

Semi-supervised learning (SSL) improves model generalization by leveraging massive
unlabeled data to augment limited labeled samples. However, currently, popular SSL evaluation …

Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

In this paper, we propose several ideas for enhancing a binary network to close its accuracy
gap from real-valued networks without incurring any additional computational cost. We first …

Few-shot object detection is an imperative and long-lasting problem due to the inherent long-tail
distribution of real-world data. Its performance is largely affected by the data scarcity of …