Deep Reasoning with Knowledge Graph for Social Relationship Understanding
Authors: Zhouxia Wang, Tianshui Chen, Jimmy Ren, Weihao Yu, Hui Cheng, Liang Lin
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on the public benchmarks demonstrate the superiority of our method over the existing leading competitors. |
| Researcher Affiliation | Collaboration | 1 School of Data and Computer Science, Sun Yat-sen University, China 2 Sense Time Research, China |
| Pseudocode | No | The paper describes the model architecture and mechanisms in detail using mathematical formulations and textual descriptions, but it does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or provide a link to a code repository for the methodology described. |
| Open Datasets | Yes | Extensive experiments on the large-scale People in Social Context (PISC) [Zhang et al., 2015] and the People in Photo Album Relation (PIPA-Relation) [Sun et al., 2017] datasets |
| Dataset Splits | Yes | For fair comparisons, we follow the standard train/val/test split released by [Li et al., 2017] to train and evaluate our GRM. Specifically, for the coarse level relationship, it divides the dataset into a training set of 13,142 images and 49,017 relationship instances, a validation set of 4,000 images and 14,536 instances and a test set of 4,000 images and 15,497 instances. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as particular CPU or GPU models, or memory specifications. |
| Software Dependencies | No | The paper mentions optimizers (SGD, ADAM) and neural network architectures (Res Net-101, VGG-16) but does not provide specific version numbers for any software dependencies or libraries required for reproduction. |
| Experiment Setup | Yes | For the GGNN propagation model, the dimension of the hidden state is set as 4,098 and that of the output feature is set as 512. The iteration time T is set as 3. |