arXiv Computer Vision
A VLM Answer Is Not an Anomaly Score: Rank Compression Across Image and Video Anomaly Detection
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arXiv Computer Vision
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A VLM Answer Is Not an Anomaly Score: Rank Compression in Training-Free Video Anomaly Detection
The paper investigates how vision‑language models (VLMs) can be used for training‑free video anomaly detection (VAD) by answering questions about video segments. It shows that the way a VLM’s output is converted into an anomaly score—either by taking only the most likely answer (generated readout) or by using the full probability distribution (probability readout)—has a significant impact on ranking performance. Across four large VLMs, the probability readout consistently outperforms the generated readout, achieving 5–13 point gains in AUROC or AP, because the generated readout compresses the rank order into only a few distinct scores.
arXiv Computer Vision
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Probe-VAD: Ordinal Likelihood Probing for Training-Free Video Anomaly Detection
Probe‑VAD introduces an ordinal binary‑probing framework that leverages frozen vision‑language models for training‑free video anomaly detection. By querying ten ordered severity thresholds and extracting YES/NO continuation likelihoods, it builds a cumulative severity profile that is converted into a continuous anomaly score with isotonic projection for ordinal consistency. Experiments on public benchmarks show that this simple interface yields superior performance at low computational cost, avoiding the limitations of caption‑based compression or restricted numerical scoring.
arXiv AI
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Agentic Ensemble with Vision-Language Models for Visual Anomaly Detection
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arXiv Computer Vision
Oct 9
A Unified Score Matching Paradigm for Video Anomaly Detection and Anticipation
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.
Hugging Face Trending Papers
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A Unified Score Matching Paradigm for Video Anomaly Detection and Anticipation
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...
arXiv AI
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Parser-Free VLM Verification for Federated Weakly Supervised Video Anomaly Detection
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.