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..
$F^3Set$: Towards Analyzing Fast, Frequent, and Fine-grained Events from Videos
Authors: Zhaoyu Liu, Kan Jiang, Murong Ma, Zhe Hou, Yun Lin, Jin Song Dong
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated popular temporal action understanding methods on F3Set, revealing substantial challenges for existing techniques. Additionally, we propose a new method, F3ED, for F3 event detections, achieving superior performance. The dataset, model, and benchmark code are available at https: //github.com/F3Set/F3Set. Leveraging F3Set, we extensively evaluate existing temporal action understanding methods, aiming to reveal the challenges of F3 event understanding. To provide guidelines for future research, we conduct a number of ablation studies on modeling choices. In this section, we benchmark existing temporal action understanding methods, including TAL, TAS, and TASpot, on the F3Set dataset and conduct a series of ablation studies. |
| Researcher Affiliation | Academia | Zhaoyu Liu1,2, Kan Jiang2, Murong Ma2, Zhe Hou3, Yun Lin4, Jin Song Dong2 1Ningbo University 2 National University of Singapore 3 Grif๏ฌth University 4 Shanghai Jiao Tong University EMAIL, EMAIL EMAIL, lin EMAIL, EMAIL |
| Pseudocode | No | The paper describes the F3ED model architecture in Section 4 with components like Video Encoder, Event Localizer, Multi-label Event Classifier, and Contextual module using mathematical formulations and descriptive text, but it does not contain a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | The dataset, model, and benchmark code are available at https: //github.com/F3Set/F3Set. |
| Open Datasets | Yes | To advance research in video understanding, we introduce F3Set, a benchmark that consists of video datasets for precise F3 event detection. The dataset, model, and benchmark code are available at https: //github.com/F3Set/F3Set. |
| Dataset Splits | Yes | We employ a training, validation, and testing split of 3:1:1, with the training and validation sets drawn from the same video sources, while the test set features clips from distinct videos. |
| Hardware Specification | No | Our proposed F3ED model... can be trained quickly on a single GPU. However, it does not specify the model or type of GPU used. |
| Software Dependencies | No | The paper mentions 'We implement and train models on F3Set in an end-to-end manner.' and 'For more implementation details, please refer to Appendix F.' but does not provide specific software names with version numbers in the main text. |
| Experiment Setup | No | The paper states 'The default model takes stride size 2 and clip length 96.' and mentions referring to Appendix F for more implementation details. However, it does not provide concrete hyperparameter values like learning rate, batch size, number of epochs, or optimizer settings in the main text. |