Computer vision is becoming an increasingly trendy word in the area of image processing.
With the emergence of computer vision applications, there is a significant demand to …

In the Internet-of-Things (IoT) era, billions of sensors and devices collect and process data
from the environment, transmit them to cloud centers, and receive feedback via the Internet …

Diffusion models excel at text-to-image generation, especially in subject-driven generation
for personalized images. However, existing methods are inefficient due to the subject …

Recognition in low quality face datasets is challenging because facial attributes are
obscured and degraded. Advances in margin-based loss functions have resulted in …

This paper describes the 6th Affective Behavior Analysis in-the-wild (ABAW) Competition
which is part of the respective Workshop held in conjunction with IEEE CVPR 2024. The 6th …

In recent years, face detection algorithms based on deep learning have made great
progress. Nevertheless, the effective utilization of face detectors for small and occlusion …

Typical diffusion models are trained to accept a particular form of conditioning, most
commonly text, and cannot be conditioned on other modalities without retraining. In this …

Generating synthetic datasets for training face recognition models is challenging because
dataset generation entails more than creating high fidelity images. It involves generating …

For the visible-infrared person re-identification (VIReID) task, one of the major challenges is
the modality gaps between visible (VIS) and infrared (IR) images. However, the training …

Learning discriminative face features plays a major role in building high-performing face
recognition models. The recent state-of-the-art face recognition solutions proposed to …