arXiv Machine Learning

Hyperspectral Trajectory Image for Multi-Month Trajectory Anomaly Detection

The paper introduces TITAnD, a Trajectory Image Transformer that converts dense and sparse GPS trajectories into a Hyperspectral Trajectory Image (HTI) and applies vision-based classification and segmentation for anomaly detection. It employs a Cyclic Factorized Transformer (CFT) that splits attention along within-day and across-day axes, drastically reducing computational cost and enabling multi-month analysis. Empirical results show TITAnD outperforms existing sparse and dense benchmarks, achieving higher AUC-PR and faster inference than comparable Transformers.

arXiv Computer Vision

Oct 22

MS2CAST: A Video Anomaly Detection Framework via Multi-Scale Spatial Cross-Attention and Efficient Spatiotemporal Networks

MS2CAST is a semi‑supervised video anomaly detection framework that fuses multi‑scale cross‑attention between a vision transformer (MViTv2‑S) and a CNN (ConvNeXt‑T) for spatial encoding, and replaces optical flow with two interchangeable spatiotemporal modules—SwinLSTM and VMRNN—operating in latent space. The training objective combines L1 loss, Gradient Difference Loss, and Multi‑Scale SSIM to enforce pixel‑level accuracy, sharpness, and structural fidelity. Experiments on UCSD Ped2, CUHK Avenue, and ShanghaiTech show competitive ROC‑AUC scores while using less than 1 GB of GPU memory.

By Ali Safaei, Mohammad Kazemi

llms diffusion computer-vision

arXiv Computer Vision

Sep 2

What, Where, and How: Probing Spatiotemporal Representations in Video Foundation Models

arXiv:2609.01551v1 Announce Type: new Abstract: Self-supervised video foundation models learn rich spatiotemporal representations, yet it remains unclear what visual concepts these representations en...

By Sharon S. Musa, Fereshteh Forghani, Harrish Thasarathan, Sonia Joseph, Matthew Kowal, Konstantinos G. Derpanis

llms

arXiv AI

Jul 17

VAN-AD: Visual Masked Autoencoder with Normalizing Flow For Time Series Anomaly Detection

arXiv:2603. 26842v3 Announce Type: replace-cross Abstract: Time series anomaly detection (TSAD) is essential for maintaining the reliability and security of IoT-enabled service systems.

By PengYu Chen, Shang Wan, Xiaohou Shi, Yuan Chang, Yan Sun, Sajal K. Das

llms multimodal benchmarks

arXiv AI

Jul 13

Event Stream based Multi-Modal Video Anomaly Detection: A Benchmark Dataset and Algorithms

arXiv:2607. 09114v1 Announce Type: cross Abstract: Video anomaly detection (VAD) is critical for automated surveillance but remains fragile under challenging conditions such as illumination variations, fast motion, and complex backgrounds when relying solely on visible light videos.

By Peipei Zhu, Yueqing Niu, Lin Zhu, Guanchong Niu, Yang Yu, Zheng Li

rag multimodal benchmarks

arXiv AI

Sep 7

Adaptive Multi-Granularity Temporal Modeling for Weakly Supervised Video Anomaly Detection

The paper introduces an adaptive temporal modeling framework for weakly supervised video anomaly detection that addresses the limitations of rigid Multiple Instance Learning approaches. It presents a Temporal Refinement Module using dynamic positional encoding and a learnable class token to capture long‑range dependencies, and an Event Segmentation Module that identifies event boundaries via temporal discontinuity analysis to produce discriminative event‑level representations. An adaptive similarity‑based fusion strategy replaces fixed top‑k heuristics, dynamically integrating snippet‑level and event‑level anomaly scores into video‑level predictions, and the method outperforms state‑of‑the‑art baselines on two benchmarks.

By Changyi Li, Yu Xiao

computer-vision benchmarks

arXiv Machine Learning

Oct 23

MoG-VLM: Efficient Video Anomaly Detection through Motion-Guided Vision-Language Models

MoG-VLM is a motion‑guided vision‑language model designed for efficient video anomaly detection. It uses a lightweight motion signal, Temporal Gradient Magnitude, to perform content‑aware sampling and activates dense multi‑frame VLM reasoning only around frames with high anomaly likelihood via a hierarchical dual‑stream design. The method achieves state‑of‑the‑art AUC‑ROC scores on four benchmarks and delivers a 1.31× inference speedup on XD‑Violence.

By Khaled Waleed Dawoud, Muhammad Zaigham Zaheer, Khan Muhammad, Muhammad Haris Khan

llms multimodal benchmarks

arXiv Computer Vision

Sep 2

Spatial Autoregressive Modeling of DINOv3 Embeddings for Unsupervised Anomaly Detection

arXiv:2603.02974v2 Announce Type: replace Abstract: DINO models provide rich patch-level representations that have recently enabled strong performance in unsupervised anomaly detection (UAD). Most ex...

By Ertunc Erdil, Nico Schulthess, Guney Tombak, Ender Konukoglu

rag benchmarks

arXiv AI

Oct 21

Agentic Ensemble with Vision-Language Models for Visual Anomaly Detection

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

llms agents multimodal benchmarks

arXiv AI

Aug 13

Glance, Scrutinize, and Think: Advancing Video Anomaly Detection from Training-Free to Agentic Reasoning

arXiv:2608. 11260v1 Announce Type: new Abstract: Video Anomaly Detection (VAD) aims to identify anomalous events and localize their temporal intervals.

By Shibo Gao, Peipei Yang, Xu-Yao Zhang, Linlin Huang

llms agents reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv AI

2d ago

Tiny but Trusted: Efficient Vision-Language Reasoning for Time-Series Anomaly Detection

The paper introduces VisAnomBench, a new benchmark that augments public time‑series datasets with high‑quality natural‑language anomaly explanations generated by large vision‑language models. Using this benchmark, the authors fine‑tune a parameter‑efficient VLM called VisAnomReasoner, which achieves significantly higher precision and F1 scores than existing baselines on VisAnomBench and also generalizes well to the TSB‑AD‑U benchmark.

By Xiaona Zhou, Muntasir Wahed, Tianjiao Yu, Constantin Brif, Ismini Lourentzou

llms fine-tuning multimodal benchmarks

arXiv Computer Vision

Sep 10

Bounding-Box Trajectories Matter for Video Anomaly Detection

arXiv:2605.21957v2 Announce Type: replace Abstract: Video anomaly detection is critical for public safety and security, yet remains highly challenging despite extensive research due to large variatio...

By Inpyo Song, Jangwon Lee

computer-vision safety

Hugging Face Trending Papers

Aug 5

VQ-VAD: Vector-quantized Motion Representation Learning for Human-centric Video Anomaly Detection

Video Anomaly Detection (VAD) is inherently challenging due to the scarcity of anomalies and the large visual variability in surveillance footage, including changes in lighting, viewpoint, and human appearance. To mitigate visual noise and address privacy concerns, recent work has shifted to pose-based VAD, which focuses on motion dynamics rather than raw video data.

diffusion efficiency benchmarks safety

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