As an advanced research topic in biometrics techniques, palmprint recognition has been
fully studied for more than 20 years. Due to its superiority to other biological features, ie high …

In adversarial training (AT), the main focus has been the objective and optimizer while the
model has been less studied, so that the models being used are still those classic ones in …

Out-of-distribution (OOD) detection has received much attention lately due to its importance
in the safe deployment of neural networks. One of the key challenges is that models lack …

Efficient deep palmprint recognition has become an urgent issue for the demand of personal
identification on mobile/wearable devices. Compared to other biometrics, palmprint …

At present, the fusion of different unimodal biometrics has attracted increasing attention from
researchers, who are dedicated to the practical application of biometrics. In this paper, we …

We study the problem of node classification on graphs with few-shot novel labels, which has
two distinctive properties:(1) There are novel labels to emerge in the graph;(2) The novel …

Out-of-distribution (OOD) detection is vital to safety-critical machine learning applications and
has thus been extensively studied, with a plethora of methods developed in the literature. …

Building reliable object detectors that can detect out-of-distribution (OOD) objects is critical
yet underexplored. One of the key challenges is that models lack supervision signals from …

Detecting out-of-distribution (OOD) objects is indispensable for safely deploying object
detectors in the wild. Although distance-based OOD detection methods have demonstrated …

Utilizing auxiliary outlier datasets to regularize the machine learning model has demonstrated
promise for out-of-distribution (OOD) detection and safe prediction. Due to the labor …