Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Learning Event Completeness for Weakly Supervised Video Anomaly Detection
Authors: Yu Wang, Shiwei Chen
ICML 2025 | Venue PDF | 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). |