In addition, we introduce a novel entropy model that incorporates two different hyperpriors to model cross-channel and spatial dependencies of the latent representation.
The proposed consistency regularization aligns the abstraction in the hidden layers of our model across the morph attack images which are generated from diverse domains in the wild.
However, these methods have shown a significant decline in performance when applied to real-world low-resolution benchmarks like TinyFace or SCFace.
In this paper, we present a new multi-branch neural network that simultaneously performs soft biometric (SB) prediction as an auxiliary modality and face recognition (FR) as the main task.
Convolutional Neural Networks (CNNs) have advanced existing medical systems for automatic disease diagnosis.
In this paper, we explore the robustness of vision transformers against adversarial perturbations and try to enhance their robustness/accuracy trade-off in white box attack settings.
In this paper, we intend to learn a morph detection model with high generalization to a wide range of morphing attacks and high robustness against different adversarial attacks.
In this paper, we propose a two-phase multi-expert classification method for human action recognition to cope with long-tailed distribution by means of super-class learning and without any extra information.
In this paper, we propose a domain adaptation approach to strengthen the contributions of the semantic background context.
Visual object tracking is one of the interesting and important areas in computer vision that achieves remarkable improvements in recent years.