DCAN: Improving Temporal Action Detection via Dual Context Aggregation
Authors: Guo Chen, Yin-Dong Zheng, Limin Wang, Tong Lu248-257
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on Activity Net v1.3 and THUMOS-14. DCAN obtains an average m AP of 35.39% on Activity Net v1.3 and reaches m AP 54.1% at Io U@0.5 on THUMOS-14, which demonstrates DCAN can generate high-quality proposals and achieve state-of-the-art performance. |
| Researcher Affiliation | Academia | State Key Lab for Novel Software Technology, Nanjing University, China {chenguo1177, ydzheng0331}@gmail.com, {lmwang, lutong}@nju.edu.cn |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We release the code at https://github.com/cg1177/DCAN. |
| Open Datasets | Yes | We conduct extensive experiments on Activity Net v1.3 and THUMOS-14. (Jiang et al. 2014) (Heilbron et al. 2015) |
| Dataset Splits | Yes | Activity Net v1.3. (Heilbron et al. 2015) is a large-scale action understanding dataset, which consists of 19,994 videos for training, 4,728 for validation, and 5,044 for testing, with 200 action classes. |
| Hardware Specification | Yes | Inference speed here is the seconds (s) cost Ttotal for processing a 3-minute video using an Nvidia 1080-Ti graphics card. |
| Software Dependencies | No | The paper mentions using a 'two-stream network' and 'TSN' but does not specify software libraries with version numbers (e.g., PyTorch, TensorFlow, CUDA, specific Python packages). |
| Experiment Setup | Yes | For Activity Net v1.3, [...] We set the batch size to 16 and the learning rate to 0.001 for the first 7 epochs and 0.0001 for the following 3 epochs. For THUMOS-14, [...] We set the batch size to 16 and the learning rate to 0.0004 for all 5 epochs. The Nb is set to 6 on Activity Net v1.3 and 7 on THUMOS-14. The Nbase, Nsample, rsmooth and G are set to 3, 32, 3 and 2. In the post-processing, the Soft-NMS threshold is set to 0.5 to pick the top Nfinal confident predictions, where Nfinal is 100 for Activity Net v1.3 and 200 for THUMOS-14. [...] γ is a hyperparameter for adjusting the compatibility of boundary scores and matching scores and is set as 1.5 on THUMOS-14 and 0.8 on Activity Net v1.3, respectively. |