1 citations · 2 across the 6 of their papers we have counts for
6 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…
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…
DCSCR: A Class-Specific Collaborative Representation based Network for Image Set Classification
Xizhan Gao, Wei Hu
Image set classification (ISC), which can be viewed as a task of comparing similarities between sets consisting of unordered heterogeneous images with variable quantities and quali…
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…
Discriminative Feature Learning Framework with Gradient Preference for Anomaly Detection
Muhao Xu, Xueying Zhou, Xizhan Gao +2
Unsupervised representation learning has been extensively employed in anomaly detection, achieving impressive performance. Extracting valuable feature vectors that can remarkably i…