A Causal Inference Look at Unsupervised Video Anomaly Detection
Authors: Xiangru Lin, Yuyang Chen, Guanbin Li, Yizhou Yu1620-1629
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on six standard benchmark datasets show that our proposed method significantly outperforms previous state-of-the-art methods, demonstrating our framework s effectiveness. |
| Researcher Affiliation | Academia | 1Sun Yat-sen University 2The University of Hong Kong |
| Pseudocode | No | The paper describes its methods in text and mathematical formulas but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We evaluate our method on four benchmark datasets, namely UCSD dataset (Mahadevan et al. 2010), Subway surveillance dataset (Adam et al. 2008), UMN dataset (Mehran, Oyama, and Shah 2009), and Avenue dataset (Lu, Shi, and Jia 2013). |
| Dataset Splits | No | We train the model on a sampled training set and evaluate our model on the full dataset with the ground truth used in the evaluation only. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | We set the default setting on the UCSD datasets to: Ns = 16, d = 1024, (a%, b%) = (5%, 20%) to balance the computational cost and performance. ... a and b are typically set to 5 and 20 respectively. |