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 | Conference PDF | Archive PDF | Plain Text | 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. |