Are all Frames Equal? Active Sparse Labeling for Video Action Detection
Authors: Aayush Rana, Yogesh Rawat
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 aayushjr@knights.ucf.edu yogesh@crcv.ucf.edu 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). |