The real-world data tends to be heavily imbalanced and severely skew the data-driven deep
neural networks, which makes Long-Tailed Recognition (LTR) a massive challenging task. …

3D Morphable Models (3DMMs) demonstrate great potential for reconstructing faithful and
animatable 3D facial surfaces from a single image. The facial surface is influenced by the …

Real-world data universally confronts a severe class-imbalance problem and exhibits a long-tailed
distribution, ie, most labels are associated with limited instances. The naïve models …

The creation of 4D avatars (ie, animated 3D avatars) from text description typically uses text-to-image
(T2I) diffusion models to synthesize 3D avatars in the canonical space and …

The evaluation of 3D face reconstruction results typically relies on a rigid shape alignment
between the estimated 3D model and the ground-truth scan. We observe that aligning two …

Spatiotemporal predictive learning (ST-PL) is a hotspot with numerous applications, such as
object movement and mete-orological prediction. It aims at predicting the subsequent …

Image retrieval has become an increasingly appealing technique with broad multimedia
application prospects, where deep hashing serves as the dominant branch towards low storage …

Deep hashing has shown promising performance in large-scale image retrieval. The hashing
process utilizes Deep Neural Networks (DNNs) to embed images into compact continuous …

Development of computer science has led to the blooming of artificial intelligence (AI), and
neural networks are the core of AI research. Although mainstream neural networks have done …

Real-world data usually suffers from severe class imbalance and long-tailed distributions,
where minority classes are significantly underrepresented compared to the majority ones. …