Text-to-image synthesis has recently seen significant progress thanks to large pretrained
language models, large-scale training data, and the introduction of scalable model families …

Artificial intelligence (AI) has been successfully applied in industry for decades, ranging from
the emergence of expert systems in the 1960s to the wide popularity of deep learning today …

Advancements in smartphone sensor technologies have significantly enriched the field of
human activity recognition, facilitating a wide array of applications from health monitoring to …

Tensors, or multidimensional arrays, are data structures that can naturally represent visual
data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic …

From holistic low-dimension feature-based segmentation to deep polynomial neural
networks, Face Recognition (FR) accuracy has increased dramatically since its early days …

The development of deep learning-based biometric models that can be deployed on devices
with constrained memory and computational resources has proven to be a significant …

Abstract Facial Emotion Recognition (FER) has gained popularity in recent years due to its
many applications, including biometrics, detection of mental illness, understanding of human …

Blind deconvolution (BD) has been demonstrated to be an efficacious approach for
extracting bearing fault-specific features from vibration signals under strong background …

Tensor networks (TNs) and neural networks (NNs) are two fundamental data modeling
approaches. TNs were introduced to solve the curse of dimensionality in large-scale tensors …

This paper proposes a novel Unified Feature Optimization (UFO) paradigm for training and
deploying deep models under real-world and large-scale scenarios, which requires a …