Cross-media Multi-level Alignment with Relation Attention Network
Authors: Jinwei Qi, Yuxin Peng, Yuxin Yuan
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on 2 cross-media datasets, and compare with 10 state-of-the-art methods to verify the effectiveness of proposed approach. |
| Researcher Affiliation | Academia | Jinwei Qi, Yuxin Peng and Yuxin Yuan Institute of Computer Science and Technology, Peking University, Beijing 100871, China pengyuxin@pku.edu.cn |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for their methodology is publicly available. |
| Open Datasets | Yes | Flickr-30K dataset [Young et al., 2014]... MS-COCO dataset [Lin et al., 2014] |
| Dataset Splits | Yes | Following [Peng et al., 2017; Tran et al., 2016], there are 1,000 pairs in testing set and 1,000 pairs for validation, while the rest are for training. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models) used for running the experiments. |
| Software Dependencies | No | Our proposed CRAN approach is implemented by Torch. (No version number for Torch or other software dependencies are provided.) |
| Experiment Setup | Yes | The length of sequence is set as 201. There are three convolutional layers in Char-CNN, and the parameter combinations are (384, 4), (512, 4) and (2048, 4). The outputs of Char-CNN are processed by an LSTM network. Their output dimension is 2048. ...all the margins α in loss functions are set to 1. We set K = 3 for local and relation alignment in cross-media similarity measurement. The learning rate of our proposed approach is decreased by a half each 50 epochs, while it is initialized as 0.0004. |