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..
Visual Data Synthesis via GAN for Zero-Shot Video Classification
Authors: Chenrui Zhang, Yuxin Peng
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on four video datasets demonstrate that our approach can improve the zero-shot video classification performance significantly. |
| Researcher Affiliation | Academia | Institute of Computer Science and Technology, Peking University, Beijing 100871, China |
| Pseudocode | Yes | Algorithm 1 Training process of the proposed framework |
| Open Source Code | No | The paper links to PyTorch (1http://pytorch.org/) but does not provide a specific link or statement about releasing the source code for their proposed methodology. |
| Open Datasets | Yes | HMDB51 [Kuehne et al., 2013], UCF101 [Soomro et al., 2012], Olympic Sports [Niebles et al., 2010] and Columbia Consumer Video (CCV) [Jiang et al., 2011] |
| Dataset Splits | Yes | 50/50 for every dataset, i.e., video feature of 50% categories are used for model training and the other 50% categories are held unseen until test time. We take the average accuracy and standard deviation as evaluation metrics and report the results over 50 independent splits generated randomly. |
| Hardware Specification | No | The paper does not explicitly mention the hardware specifications (e.g., specific GPU or CPU models) used for running the experiments. |
| Software Dependencies | No | Our model is implemented with PyTorch1. We adopt GloVe [Pennington et al., 2014] trained on Wikipedia with more than 2.2 million unique vocabularies to obtain semantic embedding and its dimension is 300. |
| Experiment Setup | Yes | We train our framework for 300 epochs using Adam optimizer with momentum 0.9. We initialize the learning rate to 0.01 and decay it every 50 epochs by a factor of 0.5. Both λ1 and λ2 are set to 1. |