Event Video Mashup: From Hundreds of Videos to Minutes of Skeleton
Authors: Lianli Gao, Peng Wang, Jingkuan Song, Zi Huang, Jie Shao, Heng Shen
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach on a real-world You Tube event dataset collected by ourselves. The extensive experimental results demonstrate the effectiveness of the proposed framework. |
| Researcher Affiliation | Academia | 1University of Electronic Science and Technology of China, Chengdu 611731, China. 2The University of Queensland, QLD 4072, Australia. 3Columbia University, NY 10027, USA. |
| Pseudocode | No | The paper describes algorithms and methods in prose but does not include structured pseudocode or algorithm blocks. |
| 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 | No | The paper states, "we collected 1,600 videos from the Youtube" and "we construct a new dataset", but it does not provide concrete access information (e.g., link, DOI, or repository) for this dataset to be publicly available. |
| Dataset Splits | No | The paper does not specify explicit training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using "the library provided by Zhu et al. (Zhu et al. 2008)" but does not specify version numbers for this or any other software dependencies. |
| Experiment Setup | Yes | For near-duplicate detection and semantic reference, we use 0.25 as the threshold to determine whether two normalized feature vectors are near-duplicates. Also, we set λ1 = 0.7 and λ2 = 0.3 to emphasize content importance and semantic diversity. |