Existing public face image datasets are strongly biased toward Caucasian faces, and other
races (eg, Latino) are significantly underrepresented. The models trained from such datasets …

Feature extraction and matching are two crucial components in person Re-Identification
(ReID). The large pose deformations and the complex view variations exhibited by the …

Person re-identification (re-ID) and attribute recognition share a common target at learning
pedestrian descriptions. Their difference consists in the granularity. Most existing re-ID …

Person re-identification is a challenging task mainly due to factors such as background
clutter, pose, illumination and camera point of view variations. These elements hinder the …

Federated learning (FL) is a promising technique for resolving the rising privacy and security
concerns. Its main ingredient is to cooperatively learn the model among the distributed …

Human-centric perceptions (eg, pose estimation, human parsing, pedestrian detection,
person re-identification, etc.) play a key role in industrial applications of visual models. While …

The huge variance of human pose and the misalignment of detected human images
significantly increase the difficulty of person Re-Identification (Re-ID). Moreover, efficient Re …

Person re-identification is a challenging task due to various complex factors. Recent studies
have attempted to integrate human parsing results or externally defined attributes to help …

Abstract Low Rank Regularization (LRR), in essence, involves introducing a low rank or
approximately low rank assumption to target we aim to learn, which has achieved great …

In-context learning provides a new perspective for multi-task modeling for vision and NLP.
Under this setting the model can perceive tasks from prompts and accomplish them without …