Skip to content
Log in
Sponsored AI AM CLAUDE, A Beginner's Guide: Learn the basics of working with AI AI AM CLAUDE, A Beginner's Guide: Learn the basics of working with AI

Author

Chao-Long Zhang

View on Semantic Scholar

4 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Conference Oct 2026

HTNet-SDGCN: hierarchical transformer with structural-dynamic dual graph convolutional network for micro-expression recognition

Micro-expression recognition (MER) is a fundamental yet challenging task in affective computing due to the subtle, transient, and localized nature of spontaneous facial muscle movements. Although Vision Transformers effectively extract MER features, spatial regions are often processed in isolation, limiting the ability...

Xi-Chang An, Chao-Long Zhang, Yuan-Ping Xu et al. · 0 citations
Conference Aug 2026

A Calibrated Hybrid Edge Framework for Elevator Safety Warning with Statistical Anomaly Detection

This paper presents a calibrated hybrid edge framework for elevator safety warning. The framework combines interpretable safety rules, robust statistical anomaly scoring, one-class anomaly-detection baselines, temporal persistence, and structured event logging on a Raspberry Pi platform. The prototype integrates accele...

Zhe Zhang, Yuan-Bing Ouyang, Le-Tian Wei et al. · 0 citations
Conference Aug 2026

Adapting Frozen Large Vision Models for Silhouette-Based Gait Recognition

In practical gait recognition scenarios where only silhouette sequences are available without original videos, directly applying appearance-driven BigGait frameworks often leads to a mismatch between methodology and data conditions. To leverage the prior advantages of frozen Large Vision Models (LVMs) for universal fea...

En-Ze Shen, Chao-Long Zhang, Yuan-Ping Xu et al. · 0 citations
Conference Aug 2026

Mamba-Guided Diffusion for Prediction-Based Industrial Video Anomaly Detection

Industrial video anomaly detection must handle more than one failure mode: motion, position, rhythm, and object state may all drift We present MG-DDAD, a prediction-based normal-future anomaly detector built on a re-implementation of the DiffiiMa predictor. Given 16 frames of RGB history, it predicts a gap-4 normal-fut...

Jia-Yu Li, Yu-Shan Pan, Wei-Hua Liu et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.