Diverse Sequential Subset Selection for Supervised Video Summarization
Authors: Boqing Gong, Wei-Lun Chao, Kristen Grauman, Fei Sha
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive results of summarizing videos from 3 datasets demonstrate the superior performance of our method, compared to not only existing unsupervised methods but also naive applications of the standard DPP model. |
| Researcher Affiliation | Academia | Boqing Gong Department of Computer Science University of Southern California Los Angeles, CA 90089 boqinggo@usc.edu Wei-Lun Chao Department of Computer Science University of Southern California Los Angeles, CA 90089 weilunc@usc.edu Kristen Grauman Department of Computer Science University of Texas at Austin Austin, TX 78701 grauman@cs.utexas.edu Fei Sha Department of Computer Science University of Southern California Los Angeles, CA 90089 feisha@usc.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code or explicitly state its availability. |
| Open Datasets | Yes | We benchmark various methods on 3 video datasets: the Open Video Project (OVP), the Youtube dataset [24], and the Kodak consumer video dataset [32]. |
| Dataset Splits | Yes | For each dataset, we randomly choose 80% of the videos for training and use the remaining 20% for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library names with versions). |
| Experiment Setup | Yes | The dimension of our linear transformed features W fi is 10, 40 and 100 for OVP, Youtube, and Kodak, respectively. For the neural network, we use 50 hidden units and 50 output units. |