Learning Event Completeness for Weakly Supervised Video Anomaly Detection
Authors: Yu Wang, Shiwei Chen
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our LEC-VAD demonstrates remarkable advancements over the current state-of-the-art methods on two benchmark datasets XD-Violence and UCF-Crime. Extensive evaluations on the XD-Violence and UCF-Crime datasets have shown that our LEC-VAD achieves state-of-the-art performance. |
| Researcher Affiliation | Collaboration | 1School of Computer Science and Technology, Tongji University, Shanghai, China. 2Department of R&D Data, Microsoft Asia-Pacific Technology CO Ltd, Shanghai, China.. Correspondence to: Yu Wang <EMAIL>. |
| Pseudocode | No | The paper describes the methodology using textual explanations and mathematical equations, but it does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about the release of source code, nor does it provide any links to a code repository or mention code in supplementary materials. |
| Open Datasets | Yes | Datasets. UCF-Crime (Sultani et al., 2018) consists of 1900 untrimmed videos... XD-Violence (Wu et al., 2020) is a larger-scale benchmark comprising 4754 untrimmed videos... |
| Dataset Splits | Yes | UCF-Crime ... We adhere to a standard data split, where the training set and testing set comprise 1610 and 290 videos, respectively. XD-Violence ... 3954 videos and 800 videos are employed for training and testing respectively. |
| Hardware Specification | No | The paper mentions using 'pre-trained image encoders' and 'multiple vision encoders including I3D (Carreira & Zisserman, 2017), C3D (Tran et al., 2015), and the CLIP (Vi T-B/16)' to extract features, but does not specify the hardware (e.g., GPU, CPU models) used for training or running the experiments. |
| Software Dependencies | No | The paper mentions using a 'pre-trained text encoder of CLIP (Vi T-B/16)' and the 'Adam W optimizer,' but it does not provide specific version numbers for software libraries, programming languages, or frameworks used for implementation. |
| Experiment Setup | Yes | Implementation Details. The value of K is determined as K = max( T/16 , 1), and the momentum coefficient η is set to 0.99. We adopt the Adam W optimizer and train our LEC-VAD with a batch size of 64. The learning rate is set to 3e-5 and the model is trained for 10 epochs. We apply NMS with an Io U threshold of 0.5, and set the threshold rcls, and rano to 0.1 and 0.2. The hyper-parameters β, λ, γ, and m are explored in the experimental sections (Figure 5). |