GIF Thumbnails: Attract More Clicks to Your Videos
Authors: Yi Xu, Fan Bai, Yingxuan Shi, Qiuyu Chen, Longwen Gao, Kai Tian, Shuigeng Zhou, Huyang Sun3074-3082
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on our built dataset show that GEVADEN significantly outperforms several baselines, including video-summarization and highlight-detection based ones. Furthermore, we develop a pilot application of the proposed model on an online video platform with 9814 videos covering 1231 hours, which shows that our model achieves a 37.5% CTR improvement over traditional image thumbnails. |
| Researcher Affiliation | Collaboration | 1Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, China 2Bilibili, China 3Department of Computer Science, UNC Charlotte |
| Pseudocode | No | The paper describes the model architecture and components but does not provide pseudocode or algorithm blocks. |
| Open Source Code | Yes | Codes and Appendix are available at https://github.com/xyiyy/GIF-Thumbnails |
| Open Datasets | No | While the paper describes building a new dataset ('we build the first GIF thumbnails benchmark dataset'), it does not provide concrete access information (e.g., a specific link, DOI, or repository) for this dataset to be publicly accessed. |
| Dataset Splits | Yes | We randomly split the annotated videos from the benchmark dataset into three parts: 857 for training, 106 for validation, and 107 for testing. |
| Hardware Specification | Yes | experiments are conducted on eight NVIDIA Tesla P40s. |
| Software Dependencies | Yes | Networks are implemented in Pytorch v1.3 |
| Experiment Setup | Yes | We train networks via Aadm (Kingma and Ba 2014) optimizer with an initial learning rate of 3 10 4 and a mini-batch size of 16. |