# Updated version: A Video Anomaly Detection Framework based on Appearance-Motion Semantics Representation Consistency

arXiv: https://arxiv.org/abs/2303.05109 · HTML: https://mlsanity.com/paper/2303.05109/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 9 Mar 2023.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016)
- [Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/1904.02639/) (2019)
- [Future Frame Prediction for Anomaly Detection -- A New Baseline](https://mlsanity.com/paper/1712.09867/) (2017)
- [Learning Memory-guided Normality for Anomaly Detection](https://mlsanity.com/paper/2003.13228/) (2020)
- [Learning Temporal Regularity in Video Sequences](https://mlsanity.com/paper/1604.04574/) (2016)
- [A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction](https://mlsanity.com/paper/2108.06852/) (2021)
- [Anomaly Detection in Video Sequence with Appearance-Motion Correspondence](https://mlsanity.com/paper/1908.06351/) (2019)
- [Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm](https://mlsanity.com/paper/2004.07657/) (2020)
- [Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Events](https://mlsanity.com/paper/2008.11988/) (2020)
- [Future Frame Prediction Using Convolutional VRNN for Anomaly Detection](https://mlsanity.com/paper/1909.02168/) (2019)

## Built on by (most cited first, 8 of 8)

- [Deep Learning for Video Anomaly Detection: A Review](https://mlsanity.com/paper/2409.05383/) (2024)
- [The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM](https://mlsanity.com/paper/2507.21649/) (2025)
- [Divide and Conquer in Video Anomaly Detection: A Comprehensive Review and New Approach](https://mlsanity.com/paper/2309.14622/) (2023)
- [TRACES: Temporal Recall with Contextual Embeddings for Real-Time Video Anomaly Detection](https://mlsanity.com/paper/2511.00580/) (2025)
- [A brief introduction to a framework named Multilevel Guidance-Exploration Network](https://mlsanity.com/paper/2312.04119/) (2023)
- [Patch Spatio-Temporal Relation Prediction for Video Anomaly Detection](https://mlsanity.com/paper/2403.19111/) (2024)
- [Semore: VLM-guided Enhanced Semantic Motion Representations for Visual Reinforcement Learning](https://mlsanity.com/paper/2512.05172/) (2025)
- [Forward Consistency Learning with Gated Context Aggregation for Video Anomaly Detection](https://mlsanity.com/paper/2601.18135/) (2026)

## Abstract (arXiv)

Video anomaly detection is an essential but challenging task. The prevalent methods mainly investigate the reconstruction difference between normal and abnormal patterns but ignore the semantics consistency between appearance and motion information of behavior patterns, making the results highly dependent on the local context of frame sequences and lacking the understanding of behavior semantics. To address this issue, we propose a framework of Appearance-Motion Semantics Representation Consistency that uses the gap of appearance and motion semantic representation consistency between normal and abnormal data. The two-stream structure is designed to encode the appearance and motion information representation of normal samples, and a novel consistency loss is proposed to enhance the consistency of feature semantics so that anomalies with low consistency can be identified. Moreover, the lower consistency features of anomalies can be used to deteriorate the quality of the predicted frame, which makes anomalies easier to spot. Experimental results demonstrate the effectiveness of the proposed method.