Abstract Human Pose Estimation (HPE) is widely used in various fields, including motion
analysis, healthcare, and virtual reality. However, the great expenses of labeled real-world …

Large-scale pre-trained Vision & Language (VL) models have shown remarkable
performance in many applications, enabling replacing a fixed set of supported classes with …

Abstract Domain adaptation methods for 2D human pose estimation typically require
continuous access to the source data during adaptation, which can be challenging due to …

Abstract Unsupervised Domain Adaptation Regression (DAR) aims to bridge the domain
gap between a labeled source dataset and an unlabelled target dataset for regression …

Abstract 3D human pose estimation is a key component of clinical monitoring systems. The
clinical applicability of deep pose estimation models, however, is limited by their poor …

Automatically detecting human-object interactions (HOIs) from an image is a very important
but challenging task in computer vision. One of the significant problems in HOI detection is …

Human silhouette extraction is a fundamental task in computer vision with applications in
various downstream tasks. However, occlusions pose a significant challenge, leading to …

Pose estimation is remarkably successful under supervised learning, but obtaining
annotations, especially for new deployments, is costly and time-consuming. This work …

It is a challenging task to reconstruct human pose from millimeter wave (mmWave) radar
point clouds due to their sparsity and sensitivity to multipath noise. In order to enhance the …

Deep learning-based recognition systems are deployed at scale for real-world applications
that inevitably involve our social life. Although of great support when making complex …