Existing person re-identification (Re-ID) methods usually rely heavily on large-scale
thoroughly annotated training data. However, label noise is unavoidable due to inaccurate …

Monocular 3D object detection (Mono3D) has achieved unprecedented success with the
advent of deep learning techniques and emerging large-scale autonomous driving datasets …

Unsupervised domain adaptation (UDA) techniques for person re-identification (ReID) have
been extensively studied to facilitate the transfer of knowledge from labeled source domains …

Person Re-Identification (ReID) across non-overlapping cameras is a challenging task, and
most works in prior art rely on supervised feature learning from a labeled dataset to match …

In dairy farming, ensuring the health of each cow and minimizing economic losses requires
individual monitoring, achieved through cow Re-Identification (Re-ID). Computer vision …

Gait recognition is an important AI task, which has been progressed rapidly with the
development of deep learning. However, existing learning based gait recognition methods …

The pursuit of accurate 3D hand pose estimation stands as a keystone for understanding
human activity in the realm of egocentric vision. The majority of existing estimation methods …

The use of pseudo-labels prevails to tackle Unsupervised Domain Adaptive (UDA) Re-
Identification (re-ID) with the best performance. Indeed, this family of approaches has given …

Unsupervised Domain Adaptation (UDA) for re-identification (re-ID) is a challenging task: to
avoid a costly annotation of additional data, it aims at transferring knowledge from a domain …

Recent researches on unsupervised person re-identification~(reID) have demonstrated that
pre-training on unlabeled person images achieves superior performance on downstream …