Deeply-Supervised CNN Model for Action Recognition with Trainable Feature Aggregation
Authors: Yang Li, Kan Li, Xinxin Wang
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on two action recognition datasets: HMDB51 and UCF101. Results show that our model outperforms the state-of-the-art methods. |
| Researcher Affiliation | Academia | Yang Li, Kan Li , Xinxin Wang School of Computer Science, Beijing Institute of Technology, Beijing, China {yanglee, likan, wangxx}@bit.edu.cn |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We evaluate the proposed models on two popular action classification benchmarks, HMDB51 [Kuehne et al., 2011] and UCF101 [Soomro et al., 2012]. |
| Dataset Splits | Yes | Both datasets are split into three parts, and we report final performance averaged over all three splits. |
| Hardware Specification | Yes | We train it on a PC with 3.4GHz CPU, a TITAN X GPU, and 64G RAM. |
| Software Dependencies | No | The paper only mentions software names (Inception with Batch Normalization, Adam) but does not provide specific version numbers for key libraries or frameworks. |
| Experiment Setup | Yes | Thus, we finally selected T = 10, which is a good trade-off between accuracy and efficiency. For the parameters of the aggregation module, we experiment with different values of K (K = 16, 32 and 64) and obtain the best performance when K = 32. The network is trained with a momentum of 0.9 and a weight of decay of 4e 5. learned by back-propagating the loss function defined in Eq. 8 using the Adam [Kingma and Ba, 2014] with ϵ = 1e 4. |