1 citations · 1 across the 5 of their papers we have counts for
5 papers
Few-Shot Video Recognition via Hierarchical Metric Learning
Jiaxin Zhang, Haoran Gao, Xizhan Gao +3
Few-shot action recognition (FSAR) aims to recognize unseen action categories with only a small number of annotated video samples. Recent works typically apply single-prototype sup…
CFR-Net:Collaborative Feature Refinement Network for Medical Image Anomaly Detection
Zihan Nie, Muhao Xu, Wei Feng +7
Medical image anomaly detection remains challenging because networks pretrained on natural images often exhibit limited adaptability to medical images, where abnormal patterns appe…
Learning Language-Driven Sequence-Level Modal-Invariant Representations for Video-Based Visible-Infrared Person Re-Identification
Xiaomei Yang, Antai Liu, Xizhan Gao +3
The core of video-based visible-infrared person re-identification (VVI-ReID) lies in learning sequence-level modal-invariant representations across different modalities. Recent res…
CLIP4VI-ReID: Learning Modality-shared Representations via CLIP Semantic Bridge for Visible-Infrared Person Re-identification
Xiaomei Yang, Xizhan Gao, Sijie Niu +4
This paper proposes a novel CLIP-driven modality-shared representation learning network named CLIP4VI-ReID for VI-ReID task, which consists of Text Semantic Generation (TSG), Infra…
Normality Prior Guided Multi-Semantic Fusion Network for Unsupervised Image Anomaly Detection
Muhao Xu, Xueying Zhou, Xizhan Gao +3
Recently, detecting logical anomalies is becoming a more challenging task compared to detecting structural ones. Existing encoder decoder based methods typically compress inputs in…