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..
Weakly-Supervised Spatio-Temporal Anomaly Detection in Surveillance Video
Authors: Jie Wu, Wei Zhang, Guanbin Li, Wenhao Wu, Xiao Tan, Yingying Li, Errui Ding, Liang Lin
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive qualitative and quantitative evaluations to demonstrate the effectiveness of the proposed approach and analyze the key factors that contribute more to handle this task. |
| Researcher Affiliation | Collaboration | 1Sun Yat-sen University 2Baidu Inc. 3Byte Dance Inc. |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements or links about the availability of source code. |
| Open Datasets | No | To this end, we build a new dataset (denoted as ST-UCF-Crime) that annotates spatio-temporal bounding boxes for abnormal events in UCF-Crime [Sultani et al., 2018]... Furthermore, we contribute a new dataset, namely Spatio-Temporal Road Accident (abbreviated as STRA)... The paper states they 'contribute' these datasets but does not provide concrete access information (e.g., a link, DOI, or repository) for the new annotations or the STRA dataset. It mentions 'We provide more details in the appendix', but the appendix is not provided. |
| Dataset Splits | No | The paper mentions 'We randomly choose 30 positive and 30 negative bags to construct a mini-batch' for training, and uses 'abnormal testing videos' for evaluation, but does not specify the explicit train/validation/test splits (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used, such as GPU models, CPU models, or cloud computing instance types. |
| Software Dependencies | No | The paper mentions using 'C3D [Tran et al., 2015]' and 'Adam optimizer' but does not provide specific version numbers for these or other software libraries (e.g., Python, PyTorch, TensorFlow). |
| Experiment Setup | Yes | We randomly choose 30 positive and 30 negative bags to construct a mini-batch, and the number of instances per bag is limited to 200. The total loss is optimized via Adam optimizer with the learning rate of 0.0005. |