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Nowadays gender classification which plays a vital role in face recognition systems is one of the main matters in computer vision. It is difficult to classify the gender from facial images when dealing with unconstrained images in a cross-dataset protocol. In this work, we propose two...
The computer vision community considers the pose-invariant face recognition (PIFR) as one of the most challenging applications. Many works were devoted to enhancing face recognition performance when facing profile samples. They mainly focused on 2D- and 3D-based frontalization techniques trying...
Scene classification based on convolutional neural networks (CNNs) has achieved great success in recent years. In CNNs, the convolution operation performs well in extracting local features, but its ability to capture global feature representations is limited. In vision transformer (ViT), the...
Few-shot learning aims to classify novel classes with extreme few labeled samples. Existing metric-learning-based approaches tend to employ the off-the-shelf CNN models for feature extraction, and conventional clustering algorithms for feature matching. These methods neglect the importance of...
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