Action Recognition with Joints-Pooled 3D Deep Convolutional Descriptors
Authors: Congqi Cao, Yifan Zhang, Chunjie Zhang, Hanqing Lu
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on real-world datasets show that our method generates promising results, outperforming state-of-the-art results significantly. |
| Researcher Affiliation | Academia | 1National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2School of Computer and Control Engineering, University of Chinese Academy of Sciences |
| Pseudocode | No | The paper describes the steps of the framework (e.g., 'The main procedures of our framework are as follows:'), but it does not provide a formal pseudocode block or algorithm section. |
| Open Source Code | No | The paper does not contain any statement about making source code publicly available or provide a link to a code repository. |
| Open Datasets | Yes | We evaluate our method on three public action datasets: sub JHMDB [Jhuang et al., 2013], Penn Action [Zhang et al., 2013] and Composable Activities [Lillo et al., 2014]. |
| Dataset Splits | Yes | We use the 3-fold cross validation setting provided by the dataset for experiments. We use the 50/50 trainning/testing split provided by the dataset to do experiments. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software like C3D, Linear SVM (LIBLINEAR), and HMMs, but it does not provide specific version numbers for any of these software dependencies. |
| Experiment Setup | Yes | For different splits, we use the same finetuning settings. We do finetuning using mini-batches of 30 clips, with learning rate of 0.0003. The finetuning is stopped after 5000 iterations. |