Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
An Efficient Framework for Dense Video Captioning
Authors: Maitreya Suin, A. N. Rajagopalan12039-12046
AAAI 2020 | Venue PDF | 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 EMAIL, EMAIL |
| 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-ο¬tting. |