Skeleon-Based Typing Style Learning For Person Identification
We present a novel architecture for person identification based on typing-style, constructed of adaptive non-local spatio-temporal graph convolutional network. Since type style dynamics convey meaningful information that can be useful for person identification, we extract the joints positions and then learn their movements' dynamics. Our non-local approach increases our model's robustness to noisy input data while analyzing joints locations instead of RGB data provides remarkable robustness to alternating environmental conditions, e.g., lighting, noise, etc. We further present two new datasets for typing style based person identification task and extensive evaluation that displays our model's superior discriminative and generalization abilities, when compared with state-of-the-art skeleton-based models.
Code (0)
등록된 구현이 없습니다.
Tasks
Person IdentificationSimilar Papers 제목 키워드 기반
MSP-ReID: Hairstyle-Robust Cloth-Changing Person Re-Identification
Cloth-Changing Person Re-Identification (CC-ReID) aims to match the same individual across cameras under varying clothing conditions. Existing approaches often remove apparel and focus on the head region to reduce clothi…
Person Re-IdentificationDomain Adaptive Person Re-Identification via Camera Style Generation and Label Propagation
Unsupervised domain adaptation in person re-identification resorts to labeled source data to promote the model training on target domain, facing the dilemmas caused by large domain shift and large camera variations. The …
Domain AdaptationDomain Adaptive Person Re-IdentificationPerson Re-IdentificationUnsupervised Domain AdaptationMetaScript: Few-Shot Handwritten Chinese Content Generation via Generative Adversarial Networks
In this work, we propose MetaScript, a novel Chinese content generation system designed to address the diminishing presence of personal handwriting styles in the digital representation of Chinese characters. Our approach…
Few-Shot LearningStyle Variable and Irrelevant Learning for Generalizable Person Re-identification
Recently, due to the poor performance of supervised person re-identification (ReID) to an unseen domain, Domain Generalization (DG) person ReID has attracted a lot of attention which aims to learn a domain-insensitive mo…
DiversityDomain GeneralizationGeneralizable Person Re-identificationMeta-Learning+1Distribution-aware Knowledge Prototyping for Non-exemplar Lifelong Person Re-identification
Lifelong person re-identification (LReID) suffers from the catastrophic forgetting problem when learning from non-stationary data. Existing exemplar-based and knowledge distillation-based LReID methods encounter data…
DiversityKnowledge DistillationPerson Re-IdentificationTransfer Learning