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..
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 | Venue PDF | 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. |