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..
End-to-end Multi-modal Video Temporal Grounding
Authors: Yi-Wen Chen, Yi-Hsuan Tsai, Ming-Hsuan Yang
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on the Charades-STA and Activity Net Captions datasets, and show that the proposed method performs favorably against state-of-the-art approaches. |
| Researcher Affiliation | Collaboration | 1University of California, Merced 2Phiar 3Yonsei University 4Google Research |
| Pseudocode | No | The paper describes the proposed framework using text and diagrams (Figure 1, Figure 2) but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code and models are available at https://github.com/wenz116/DRFT. |
| Open Datasets | Yes | We conduct extensive experiments on the Charades-STA [10] and Activity Net Captions [17] datasets |
| Dataset Splits | Yes | Activity Net Captions. ...The dataset is split into training, validation and testing set with a ratio of 2:1:1, resulting in 37,421, 17,505 and 17,031 video-query pairs respectively. |
| Hardware Specification | No | The paper does not specify the exact hardware (e.g., GPU models, CPU types, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions implementing the model in 'Py Torch' and using 'Adam optimizer', but does not provide specific version numbers for PyTorch or any other software libraries/dependencies. |
| Experiment Setup | Yes | The feature dimension c is set to 512. In the contrastive loss (1), the temperature parameter τ is set to 0.1. The projection head h( ) is a 2-layer MLP that project the feature to a 512-dimensional latent space. We implement the proposed model in Py Torch with the Adam optimizer and a fixed learning rate of 4 10 4. |