Diverse Sequential Subset Selection for Supervised Video Summarization
Authors: Boqing Gong, Wei-Lun Chao, Kristen Grauman, Fei Sha
NeurIPS 2014 | Venue 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 EMAIL Wei-Lun Chao Department of Computer Science University of Southern California Los Angeles, CA 90089 EMAIL Kristen Grauman Department of Computer Science University of Texas at Austin Austin, TX 78701 EMAIL Fei Sha Department of Computer Science University of Southern California Los Angeles, CA 90089 EMAIL |
| 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. |