10 papers
PARSEE-VAD: Efficient Training-Free Online Video Anomaly Detection via Proposition-Aware Reasoning and Streaming Evidence Escalation
Ji Wang, Shuangqing Zhang, Guo-Sen Xie +1
Training-free online video anomaly detection (VAD) with frozen multimodal language models faces two coupled challenges: extracting reliable current-window semantics under causal an…
Probe-VAD: Ordinal Likelihood Probing for Training-Free Video Anomaly Detection
Jiawei Gu, Qilin Zhao, Tengkuo Guo +6
Video anomaly detection (VAD) aims to localize anomalous events in untrimmed videos. Vision-language models (VLMs) provide rich visual understanding for training-free VAD, but exis…
TD-VAD: Breaking Visual Dependence in Video Anomaly Detection with Text-Driven Learning
Shuangqing Zhang, Lei-Lei Ma, Zhao Wang +5
Visual data is typically a prerequisite for training existing video anomaly detection (VAD) methods. However, obtaining sufficient annotated anomaly data for training is challengin…
CL-Anomaly: Layer-Adaptive Mixture-of-Experts with Multimodal Large Language Model for Continual Learning in Anomaly Detection
Wen Dong, Zhao Wang, Shuangqing Zhang +5
Multimodal Large Language Models (MLLMs) excel in diverse vision tasks, but full-parameter retraining is computationally expensive as real-world knowledge evolves. Existing continu…
Hierarchical Vision-Language Interaction for Facial Action Unit Detection
Yong Li, Yi Ren, Yizhe Zhang +5
Facial Action Unit (AU) detection seeks to recognize subtle facial muscle activations as defined by the Facial Action Coding System (FACS). A primary challenge w.r.t AU detection i…
Spatial frequency information fusion network for few-shot learning
Wenqing Zhao, Guojia Xie, Han Pan +2
The objective of Few-shot learning is to fully leverage the limited data resources for exploring the latent correlations within the data by applying algorithms and training a model…
LAD-Reasoner: Tiny Multimodal Models are Good Reasoners for Logical Anomaly Detection
Weijia Li, Guanglei Chu, Jiong Chen +3
Recent advances in industrial anomaly detection have highlighted the need for deeper logical anomaly analysis, where unexpected relationships among objects, counts, and spatial con…
AFANet: Adaptive Frequency-Aware Network for Weakly-Supervised Few-Shot Semantic Segmentation
Jiaqi Ma, Guo-Sen Xie, Fang Zhao +1
Few-shot learning aims to recognize novel concepts by leveraging prior knowledge learned from a few samples. However, for visually intensive tasks such as few-shot semantic segment…
Kernel-Aware Graph Prompt Learning for Few-Shot Anomaly Detection
Fenfang Tao, Guo-Sen Xie, Fang Zhao +1
Few-shot anomaly detection (FSAD) aims to detect unseen anomaly regions with the guidance of very few normal support images from the same class. Existing FSAD methods usually find…
Learning Anchor Transformations for 3D Garment Animation
Fang Zhao, Zekun Li, Shaoli Huang +5
This paper proposes an anchor-based deformation model, namely AnchorDEF, to predict 3D garment animation from a body motion sequence. It deforms a garment mesh template by a mixtur…