How Unlabeled Web Videos Help Complex Event Detection?
Authors: Huan Liu, Qinghua Zheng, Minnan Luo, Dingwen Zhang, Xiaojun Chang, Cheng Deng
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | 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. |