SIRI: Spatial Relation Induced Network For Spatial Description Resolution
Authors: peiyao wang, Weixin Luo, Yanyu Xu, Haojie Li, Shugong Xu, Jianyu Yang, Shenghua Gao
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on the Touchdown show that our method is around 24% better than the state-of-the-art method in terms of accuracy, measured by an 80-pixel radius. |
| Researcher Affiliation | Academia | Peiyao Wang Weixin Luo Yanyu Xu Shanghai Tech University {wangpy, luowx, xuyy2}@shanghaitech.edu.cn Haojie Li Dalian University of Technology hjli@dlut.edu.cn Shugong Xu Shanghai University shugong@shu.edu.cn Jianyu Yang Soochow Univerisity jyyang@suda.edu.cn Shenghua Gao gaoshh@shanghaitech.edu.cn |
| Pseudocode | No | The paper describes the system architecture and components but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code for this project is publicly available at https://github.com/wong-puiyiu/siri-sdr.1 |
| Open Datasets | Yes | We conducted all experiments on the Touch Down dataset (3), which is designed for navigation and spatial description reasoning in a real-life environment. |
| Dataset Splits | Yes | In total, this dataset contains 27, 575 samples for SDR, including 17, 878 training samples, 3, 836 validation samples and 3, 859 testing samples. |
| Hardware Specification | Yes | All the experiments are conducted with a Ge Force GTX TITAN X. |
| Software Dependencies | No | The code is implemented in Pytorch. |
| Experiment Setup | Yes | In addition, the number of training mini-batches and the learning rate are 10 and 0.0001 respectively. |