Exploring Temporally Dynamic Data Augmentation for Video Recognition
Authors: Taeoh Kim, Jinhyung Kim, Minho Shim, Sangdoo Yun, Myunggu Kang, Dongyoon Wee, Sangyoun Lee
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To verify the effectiveness of the proposed method, we conduct extensive experiments on the video recognition task, where Dyna Augment reaches a better performance than the static versions of state-of-the-art image augmentation algorithms. The experimental results also demonstrate the generalization ability of Dyna Augment. |
| Researcher Affiliation | Collaboration | Taeoh Kim1, Jinhyung Kim2, Minho Shim1, Sangdoo Yun3, Myunggu Kang1, Dongyoon Wee1, Sangyoun Lee4 1NAVER Cloud, AI Tech. 2LG AI Research 3NAVER AI Lab 4Yonsei University |
| Pseudocode | No | The paper describes the Fourier Sampling using mathematical equations (Eq. 1) and textual descriptions, but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code will be available at https://github.com/clovaai/dynaaug to ensure reproducibility. |
| Open Datasets | Yes | Specifically, we show the effectiveness of Dyna Augment on various video datasets and tasks: large-scale video recognition (Kinetics-400 and Something-Something-v2), small-scale video recognition (UCF101 and HMDB-51), fine-grained video recognition (Diving-48 and Fine Gym), video action segmentation on Breakfast, video action localization on THUMOS 14, and video object detection on MOT17Det. |
| Dataset Splits | Yes | Kinetics-400 consists of 240K training and 20K validation videos in 400 action classes. Something-Something-v2 dataset (Goyal et al., 2017) contains 169K training and 25K validation videos with 174 action classes. Both dataset consist of three train/test splits, and we use the first split. |
| Hardware Specification | Yes | All our experiments are conducted in a single machine with 8 A100 or 8 V100 GPUs. |
| Software Dependencies | No | The paper mentions using "Py Torch (Paszke et al., 2017)" but does not specify a version number for PyTorch or any other key software libraries with version numbers. |
| Experiment Setup | Yes | In Table A1, we describe all hyper-parameters and compare our reproduced baselines with the official results in Kinetics-400 (Carreira & Zisserman, 2017) dataset. In Table A2, we describe all hyper-parameters and compare our reproduced baselines with the official results in the other datasets, such as Something-Something-v2 (Goyal et al., 2017), UCF-101 (Soomro et al., 2012), HMDB-51 (Kuehne et al., 2011), Diving-48 (Li et al., 2018) (DV), and Gym288 (Shao et al., 2020) (Gym) models. |