Video Summarization via Semantic Attended Networks
Authors: Huawei Wei, Bingbing Ni, Yichao Yan, Huanyu Yu, Xiaokang Yang, Chen Yao
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that our method achieves a superior performance gain over previous methods on two benchmark datasets. |
| Researcher Affiliation | Academia | Huawei Wei, Bingbing Ni, Yichao Yan, Huanyu Yu, Xiaokang Yang Shanghai Key Laboratory of Digital Media Processing and Transmission, Shanghai Jiao Tong University weihuawei26@gmail.com,{nibingbing,yanyichao,yuhuanyu,xkyang}@sjtu.edu.cn |
| Pseudocode | Yes | Algorithm 1 Training semantic attended network |
| Open Source Code | No | The paper does not provide an explicit statement about releasing the source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | We evaluate our approach on two video datasets, Sum Me (Gygli et al. 2014) and TVSum (Song et al. 2015) annotated with text descriptions created by us. |
| Dataset Splits | Yes | For each benchmark, We randomly select 80% for training and the remaining 20% for testing. |
| Hardware Specification | Yes | All experiments are conducted on the GTX TITAN X GPU using Tensorflow (Abadi et al. 2016). |
| Software Dependencies | No | The paper mentions 'Tensorflow (Abadi et al. 2016)' but does not specify a version number for it or any other software dependencies. |
| Experiment Setup | Yes | We train our networks with Adam optimizer with initial learning rate 0.0001. All experiments are conducted on the GTX TITAN X GPU using Tensorflow (Abadi et al. 2016). Both the frame selector and the decoder of the video description model are a single-layer LSTM network, and the encoder of the video description model is a bidirectional LSTM work; all these LSTM networks include 1024 hidden units. |