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..
Temporal Gaussian Mixture Layer for Videos
Authors: Aj Piergiovanni, Michael Ryoo
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The extensive experiments on multiple datasets, including Charades and Multi THUMOS, confirm the effectiveness of TGM layers, significantly outperforming the state-of-the-arts1. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Indiana University. Correspondence to: AJ Piergiovanni <EMAIL>, Michael Ryoo <EMAIL>. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks (e.g., a figure or section labeled "Algorithm" or "Pseudocode"). |
| Open Source Code | Yes | 1Code/models: https://github.com/piergiaj/tgm-icml19 |
| Open Datasets | Yes | We conducted our experiments on both THUMOS (Jiang et al., 2014) and Multi THUMOS (Yeung et al., 2015) datasets... Charades (Sigurdsson et al., 2016b) is a large scale dataset... |
| Dataset Splits | Yes | There are 1010 validation videos and 1574 test videos. We used these continuous validation videos for the training of our models. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. It mentions using I3D and Inception V3 as base CNNs but no information on the computational resources. |
| Software Dependencies | No | The paper mentions using I3D (Carreira & Zisserman, 2017) and Inception V3 (Szegedy et al., 2016) and their pretraining datasets (Imagenet, Kinetics), but does not provide specific version numbers for these software components or any other libraries/frameworks like PyTorch or TensorFlow. |
| Experiment Setup | Yes | Our default L setting used for the TGM layers as well as the other baselines was as follows: when using I3D segment features (3 features per second from 24fps videos), the 1 layer models used L = 15 and the 3 layer models used L = 5. When using Inception V3 frame feature (at 8 fps), the 1 layer models used L = 30 and the 3 layer models used L = 10. |