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..
How Unlabeled Web Videos Help Complex Event Detection?
Authors: Huan Liu, Qinghua Zheng, Minnan Luo, Dingwen Zhang, Xiaojun Chang, Cheng Deng
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiment results over standard datasets of TRECVID MEDTest 2013 and TRECVID MEDTest 2014 demonstrate the effectiveness and superiority of the proposed framework on complex event detection. |
| Researcher Affiliation | Academia | 1MOEKLINNS Lab, Department of Computer Science, Xi an Jiaotong University, Shaanxi, China; 2School of Automation, Northwestern Polytechnical University, Shaanxi, China; 3School of Computer Science, Carnegie Mellon University, PA, USA; 4School of Electronic Engineering, Xidian University, Shaanxi, China |
| Pseudocode | Yes | Algorithm 1 Alternating algorithm for problem (1) |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | We evaluate on two large scale real-world datasets: the TRECVID MEDTest 2013 and the TRECVID MEDTest 2014... We use the Yahoo Flickr Creative Commons 100 Million Dataset (YFCC100M) [Thomee et al., 2016] as the unlabeled web videos in the experiments. ... 3http://nist.gov/itl/iad/mig/med13.cfm 4http://nist.gov/itl/iad/mig/med14.cfm |
| Dataset Splits | No | The paper mentions 'cross-validated the regularization parameters' but does not specify the explicit training, validation, and test dataset splits used for its experiments. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'CNN features... VGG' and 'VLAD encoding' but does not provide specific software names with version numbers for reproducibility. |
| Experiment Setup | Yes | We cross-validated the regularization parameters in the range of {0.01, 0.1, 1, 10, 100}. We set p = 0.8 and q = 1.2 in our experiments to achieve the best performance. |