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..
Video Cloze Procedure for Self-Supervised Spatio-Temporal Learning
Authors: Dezhao Luo, Chang Liu, Yu Zhou, Dongbao Yang, Can Ma, Qixiang Ye, Weiping Wang11701-11708
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on commonly used benchmarks show that the trained models outperform the state-of-the-art self-supervised models with significant margins. |
| Researcher Affiliation | Academia | 1Institute of Information Engineering, Chinese Academy of Sciences 2School of Cyber Security, University of Chinese Academy of Sciences 3University of Chinese Academy of Sciences |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | The experiments are conducted on UCF101 (Soomro, Zamir, and Shah 2012) and HMDB51 (Jhuang et al. 2011) datasets. |
| Dataset Splits | No | The paper refers to dataset usage and evaluation on splits (e.g., "fine-tune the action recognition model on UCF101", "The classification accuracy over 3 splits are averaged"), but it does not specify explicit percentages or sample counts for training/validation/test splits needed for reproduction. |
| 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 provide specific ancillary software details with version numbers (e.g., library or solver names with versions) needed to replicate the experiment. |
| Experiment Setup | Yes | We set the initial learning rate to be 0.01, momentum to be 0.9 and stop training after 300 epochs. ... Each frame is resized to 128 x 171 and randomly cropped to 112 x 112. |