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..
Are all Frames Equal? Active Sparse Labeling for Video Action Detection
Authors: Aayush Rana, Yogesh Rawat
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed approach on two different action detection benchmark datasets, UCF101-24 and J-HMDB-21, and observed that active sparse labeling can be very effective in saving annotation costs. |
| Researcher Affiliation | Academia | Aayush J Rana Yogesh S Rawat EMAIL EMAIL Center for Research in Computer Vision (CRCV) University of Central Florida |
| Pseudocode | Yes | The entire selection algorithm is provided Appendix. |
| Open Source Code | Yes | Project details available at https://sites.google.com/view/activesparselabeling/home |
| Open Datasets | Yes | We evaluate our approach on three different datasets, UCF-101 [13], J-HMDB [14] and You Tube-VOS [77]. |
| Dataset Splits | No | The paper states the datasets used (UCF-101, J-HMDB, YouTube-VOS) and percentages of annotated frames used for training (e.g., '1% of labelled frames'), but it does not explicitly provide details about the train, validation, and test dataset splits needed for reproduction. |
| Hardware Specification | No | The paper's checklist states 'Detailed in supplementary' for hardware specifications, but these details are not provided in the main text of the paper. |
| Software Dependencies | No | The paper states 'We implement our method in Py Torch [81]', but it does not specify the version number of PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We use Adam optimizer [83] with a batch size of 8 and train for 22K iterations in each active learning cycle (details in appendix G.3). We use dropout for generating uncertainty similar to [73] by enabling it during inference. For You Tube-VOS task, we use two existing methods [77, 84]. We use τ = 0.9 for non-active suppression and σ = 1.3 for Eq. 2 and Eq. 4, which were empirically determined (details in appendix C). |