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..
Mutually Reinforced Spatio-Temporal Convolutional Tube for Human Action Recognition
Authors: Haoze Wu, Jiawei Liu, Zheng-Jun Zha, Zhenzhong Chen, Xiaoyan Sun
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show MRST-Net yields the best performance, compared to state-of-the-art approaches. |
| Researcher Affiliation | Collaboration | National Engineering Laboratory for Brain-inspired Intelligence Technology and Application, University of Science and Technology of China; School of Remote Sensing and Information Engineering, Wuhan University; Intelligent Multimedia Group, Microsoft Research Asia |
| Pseudocode | No | No structured pseudocode or algorithm blocks are present in the paper. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Three well-known benchmarks, i.e., Kinetics400[Kay et al., 2017], UCF-101[Soomro et al., 2012], and HMDB-51[Kuehne et al., 2013], are included in the evaluations. |
| Dataset Splits | No | The paper states 'Both UCF101 and HMDB51 are provided with 3 splits for training and testing' but does not explicitly detail a validation split or its size/percentage for any dataset. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper does not specify version numbers for any software dependencies, such as programming languages, libraries, or frameworks used. |
| Experiment Setup | Yes | Our data augmentation includes random clipping on both spatial (ο¬rstly resizing the smaller video side to 256 pixels, then randomly cropping a 224 224 patch) and temporal (randomly picking the starting frame among those early enough to guarantee a desired number of frames). Batch normalization is applied to all convolutional layers. We use the Adam Gradient Descent optimizer with an initial learning rate of 1e 4 to train the MRST-related networks from scratch. The drop out ratio and weight decay rate are set to 0.5 and 5e 5. |