Exploiting Images for Video Recognition with Hierarchical Generative Adversarial Networks
Authors: Feiwu Yu, Xinxiao Wu, Yuchao Sun, Lixin Duan
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on two challenging video recognition datasets (i.e. UCF101 and HMDB51) demonstrate the effectiveness of the proposed method when compared with the existing state-of-the-art domain adaptation methods. |
| Researcher Affiliation | Academia | Feiwu Yu1, Xinxiao Wu1 , Yuchao Sun1, Lixin Duan2 1 Beijing Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology 2 Big Data Research Center, University of Electronic Science and Technology of China {yufeiwu,wuxinxiao,sunyuchao}@bit.edu.cn, lxduan@uestc.edu.cn |
| Pseudocode | Yes | Algorithm 1: Hierarchical Generative Adversarial Networks |
| Open Source Code | No | The paper does not provide any concrete access information (link, explicit statement of release) to the source code for the methodology described. |
| Open Datasets | Yes | To evaluate the performance of our method, we conduct the experiments on two complex video datasets, i.e., UCF101 [Soomro et al., 2012] and HMDB51 [Yao et al., 2011]. For the UCF101 as the target video domain, the source images come from the Stanford40 dataset [Yao et al., 2011]. For the HMDB51 as the target video domain, the source image domain consists of Standford40 dataset and HII dataset [Tanisik et al., 2016], denoted by EADs dataset. |
| Dataset Splits | Yes | We split each target video into 16-frame clips without overlap, and all the clips from all the target videos construct the video-clip domain... For each dataset, we repeat the sampling for 5 times and report the average results. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory, or detailed computer specifications used for running experiments. It mentions 'implemented based on the Caffe framework' but no hardware. |
| Software Dependencies | No | The paper mentions software like 'Caffe framework' and 'Adam solver' but does not provide specific version numbers for these or any other ancillary software components. |
| Experiment Setup | Yes | To model the two generators in Hi GAN, we deploy four-layered feed-forward neural networks activated by relu function, (i.e., 2048 1024 1024 1024 512 for Gl(f; θGl) and 512 1024 1024 2048 2048 for Gh(vf; θGh))... we set λ2 = λ4 = 100, λ1 = λ3 = 1 in Eq. (8) and Eq. (9) for all the experiments... We employ the Adam solver [Kingma and Ba, 2014] with a batch size of 64. All the networks were trained from scratch with the learning rate of 0.00002 for the low-level conditional GAN and 0.000008 for high-level conditional GAN. |