Action Knowledge Transfer for Action Prediction with Partial Videos
Authors: Yijun Cai, Haoxin Li, Jian-Fang Hu, Wei-Shi Zheng8118-8125
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on the UCF-101 and HMDB-51 datasets show that the proposed action knowledge transfer method can significantly improve the performance of action prediction, especially for the actions with small observation ratios (e.g., 10%). |
| Researcher Affiliation | Academia | Yijun Cai,1 Haoxin Li,1 Jian-Fang Hu,2 Wei-Shi Zheng2,3 1School of Electronics and Information Technology, Sun Yat-sen University, China 2School of Data and Computer Science, Sun Yat-sen University, China 3The Key Laboratory of Machine Intelligence and Advanced Computing (Sun Yat-sen University), Ministry of Education |
| Pseudocode | No | The paper includes mathematical equations and architectural diagrams (Figure 2, Figure 3) but no explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of its source code. |
| Open Datasets | Yes | We test our method on two datasets: UCF-101 (Soomro, Zamir, and Shah 2012) and HMDB-51 (Kuehne et al. 2011). |
| Dataset Splits | Yes | Following (Kong, Tao, and Fu 2017; Kong et al. 2018), we use the first 15 groups of videos in UCF-101 split-1 for model training; the next 3 groups for model validation; and the remaining 7 groups for testing. For HMDB-51, We follow the standard evaluation protocol using three training/testing splits, and report the average accuracy over three splits. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or memory used for running experiments. |
| Software Dependencies | No | The paper mentions using '3D Res Next-101' and 'Stochastic gradient descent algorithm' but does not provide specific version numbers for any software or libraries (e.g., Python, TensorFlow, PyTorch versions). |
| Experiment Setup | Yes | Stochastic gradient descent algorithm is employed for optimizing the model parameters, with a batch size of 64 and momentum rate of 0.9. We follow the suggestion in (Wang et al. 2018) and set the margin m and scaling factor s for the AM Softmax to 0.4 and 30, respectively. |