arXiv:2608.21244v2 Announce Type: replace
Abstract: Anomaly detection aims to identify observations that deviate from normal patterns. Recent work uses pretrained vision-language models (VLMs) for tr...
By Inpyo Song, Jangwon Lee
The paper proposes a lightweight federated multiple‑instance learning (MIL) framework that trains only a compact MIL scorer across distributed clients while using a frozen vision‑language model (VLM) to verify high‑scoring video segments post‑hoc. Two VLM feedback interfaces are explored: a parsed text‑generation interface and a logit‑based interface that derives a continuous anomaly score from next‑token Yes/No probabilities. Experiments on UCF‑Crime with InternVL3.5‑2B and Qwen3‑VL‑2B‑Instruct show that the logit interface consistently improves frame‑level AUC and AP over the MIL baseline without requiring temporal post‑processing, whereas the text‑generation interface is more sensitive to prompts, parsers, and model choice.
By S\'ebastien Thuau, Amira Gran, Siba Haidar, Rachid Chelouah
arXiv:2610.28560v1 Announce Type: new
Abstract: Visual anomaly detection aims to detect abnormal objects, while anomaly samples are often scarce during model training. Assembling multiple pretrained...
By Shangquan Sun, Wenjun Luo, Xu He, Yoonsoo Lee, Shijian Lu
The paper introduces Uni-DSM, a unified score-driven framework that uses denoising score matching to model video anomaly patterns for both detection and anticipation. It employs a shared noise-conditioned score transformer backbone with scene-dependent embeddings and motion-aware weighting, and differentiates between VAD and VAA through autoregressive denoising and a self-distilled denoising mechanism. Experiments on multiple benchmarks show state‑of‑the‑art performance for both tasks while maintaining high efficiency.
By Congqi Cao, Zhenhe Liang, Hanwen Zhang, Yifan Zhao, Qinyi Lv, Lingtong Min, Yanning Zhang
Cog-VADU is a training‑free framework that transforms video anomaly detection into a sequential cognitive reasoning task. It uses Chain‑of‑Anomaly Detection Thought Prompting (CoADTP) to create a recurrent reasoning chain across video segments, preserving temporal memory and distinguishing complex anomalies from high‑motion normal activities. A cross‑modal re‑ranking stage aligns textual rationales with visual embeddings to enforce semantic consistency and temporal coherence, yielding competitive zero‑shot performance on multiple VAD benchmarks.
By Mohd Ubaid Wani, Sara Atito, Josef Kittler, Muhammad Awais
Video anomaly detection (VAD) is a fundamental and safety-critical task in computer vision. Recent generative approaches detect anomalies from a distributional perspective, but remain limited by local...