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..
DCAN: Improving Temporal Action Detection via Dual Context Aggregation
Authors: Guo Chen, Yin-Dong Zheng, Limin Wang, Tong Lu248-257
AAAI 2022 | Venue PDF | 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 EMAIL, EMAIL |
| 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. |