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..
Deeply-Supervised CNN Model for Action Recognition with Trainable Feature Aggregation
Authors: Yang Li, Kan Li, Xinxin Wang
IJCAI 2018 | Venue PDF | 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 EMAIL |
| 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. |