T-C3D: Temporal Convolutional 3D Network for Real-Time Action Recognition
Authors: Kun Liu, Wu Liu, Chuang Gan, Mingkui Tan, Huadong Ma
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On two challenging benchmark datasets, UCF101 and HMDB51, our method is significantly better than state-of-the-art real-time methods by over 5.4% in terms of accuracy and 2 times faster in terms of inference speed (969 frames per second), demonstrating comparable recognition performance to the state-of-the-art methods. |
| Researcher Affiliation | Academia | Kun Liu,1 Wu Liu,1 Chuang Gan,2 Mingkui Tan,3 Huadong Ma1 1Beijing Key Lab of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, China; 2Tsinghua University, Beijing, China; 3South China University of Technology, Guangzhou, China Email: {liu kun, liuwu, mhd}@bupt.edu.cn; ganchuang1990@gmail.com; mingkuitan@scut.edu.cn |
| Pseudocode | No | The paper provides mathematical formulations and a system diagram, but it does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | The source code for the complete system as well as the pre-trained models are publicly available at https://github.com/tc3d. |
| Open Datasets | Yes | We empirically evaluate our T-C3D approach on the two public benchmark datasets for action recognition: UCF101 (Soomro, Zamir, and Shah 2012) and HMDB51 (Kuehne et al. 2011). |
| Dataset Splits | Yes | For both datasets, we adopt the three standard training/testing splits provided in original works as the evaluation scheme and report the mean accuracy over these three splits. |
| Hardware Specification | Yes | As for speed evaluation, we adopt FPS as metric and conduct experiments on a CPU (E5-2640 v3) and a K40 GPU. |
| Software Dependencies | No | The paper describes the methods and algorithms used (e.g., "mini-batch stochastic gradient descent algorithm"), but it does not specify software components with version numbers (e.g., Python 3.x, PyTorch 1.x, TensorFlow 2.x). |
| Experiment Setup | Yes | The network parameters are learned in an end-to-end fashion with the mini-batch stochastic gradient descent algorithm, where the momentum is set to 0.9 and the batch size is set to 8. The pre-trained models on Sport-1M and Kinetics are utilized to initialize network weights. We randomly initialize the last fully connected layer and add a dropout layer after the global pooling layer with high dropout ratio (set to 0.8 in experiments) to prevent over-fitting. On UCF101, the initial learning rate is 0.005 and decreased to its 1/10 every 8,000 iterations. The whole optimization procedure is stopped at 20,000 iterations. |