An Efficient Framework for Dense Video Captioning
Authors: Maitreya Suin, A. N. Rajagopalan12039-12046
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive evaluations on Activity Net captions dataset to validate our method. |
| Researcher Affiliation | Academia | Maitreya Suin, A. N. Rajagopalan Indian Institute of Technology Madras maitreyasuin21@gmail.com, raju@ee.iitm.ac.in |
| Pseudocode | No | The paper describes methods and architectures verbally and mathematically but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a link to open-source code or explicitly state that code for the described methodology is available. |
| Open Datasets | Yes | Activity Net Captions (Krishna et al. 2017) is one of the largest datasets containing multiple annotated temporal event segments and corresponding natural language sentence describing those events. |
| Dataset Splits | Yes | It contains almost 20,000 You Tube videos which include 10,024, 4,926 and 5,044 videos for training, validation and test splits, respectively. |
| Hardware Specification | No | The paper mentions using Res Net-200 for feature extraction and discusses computational costs (GFLOPs), but it does not specify any particular hardware components (e.g., GPU, CPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions Adam (Kingma and Ba 2014) as an optimizer but does not specify any software names with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We leverage Adam (Kingma and Ba 2014) with an initial learning rate of 0.001. We apply the well-known regularization technique Dropout (Srivastava et al. 2014) to regularize the training and prevent over-ļ¬tting. |