MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detection
Authors: Yingxian Chen, Zhengzhe Liu, Baoheng Zhang, Wilton Fok, Xiaojuan Qi, Yik-Chung Wu
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on two large-scale benchmarks UCF-Crime and XD-Violence manifest that our method outperforms state-of-the-art approaches. |
| Researcher Affiliation | Academia | 1Department of Electrical and Electronic Engineering, The University of Hong Kong 2The Chinese University of Hong Kong {carolcyx, cheungbh}@hku.hk, {wtfok, xjqi, ycwu}@eee.hku.hk, zzliu@cse.cuhk.edu.hk |
| Pseudocode | No | The paper describes the approach using text, figures, and equations, but does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The codes are available in https: //github.com/carolchenyx/MGFN.git. |
| Open Datasets | Yes | We consider two benchmarks in our analysis, UCF-Crime (Sultani, Chen, and Shah 2018) and XD-Violence (Wu et al. 2020). |
| Dataset Splits | No | The paper does not explicitly state specific percentages or methods for training, validation, and test splits, nor does it cite a source that defines these splits for the datasets used. |
| Hardware Specification | No | The paper states 'Our proposed method is implemented in Py Torch' but does not specify any hardware details such as GPU models, CPU types, or memory. |
| Software Dependencies | No | The paper states 'Our proposed method is implemented in Py Torch (Paszke et al. 2019)' but does not provide a specific version number for PyTorch or other libraries. |
| Experiment Setup | Yes | The hyperparameters are set as 𝑇= 32, 𝑃= 10, 𝛼= 0.1, 𝑘= 3, 𝜆1 = 𝜆2 = 1, 𝜆3 = 0.001. To train the network, we used Adam optimiser (Kingma and Ba 2015) with a weight decay of 0.0005 and a learning rate of 0.001. The batch size 𝐵 in the training is 16. |