Generating Triples With Adversarial Networks for Scene Graph Construction
Authors: Matthew Klawonn, Eric Heim
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that our model improves upon prior work in scene graph generation on state-of-the-art data sets and accepted metrics. Further, we demonstrate that our model is capable of handling a larger vocabulary size than prior work has attempted.Empirical Evaluation One goal of our evaluation is to compare our method to the current state-of-the-art in scene graph generation. As (Xu et al. 2017) sets the current state-of-the-art, we compare to their method, using metrics their work established, and on the dataset they evaluated on. |
| Researcher Affiliation | Collaboration | Matthew Klawonn Rensselaer Polytechnic Institute Dept. of Computer Science Troy, NY 12180 klawom@rpi.edu Eric Heim Air Force Research Laboratory Information Directorate Rome, NY 13441 eric.heim.1@us.af.mil |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled as "Pseudocode" or "Algorithm". |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code, nor does it include links to a code repository. |
| Open Datasets | Yes | All data comes from the Visual Genome (VG) dataset (Krishna et al. 2016), since this is the largest and highest quality dataset containing image-scene graph pairs available today and the same data that (Xu et al. 2017) use for evaluation. |
| Dataset Splits | No | The paper states: "The first split exactly matches that of (Xu et al. 2017), which is a 70-30 train-test split of the dataset..." and mentions a validation set for tuning, but does not specify the percentage or size of the validation split. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU models, CPU types, or server configurations) used for running the experiments. |
| Software Dependencies | No | The paper mentions using "Adam stochastic gradient algorithm" and "layer normalization", but does not provide specific version numbers for any software libraries, frameworks (like PyTorch or TensorFlow), or programming languages. |
| Experiment Setup | Yes | Following the example of (Gulrajani et al. 2017), we use the Adam stochastic gradient algorithm (Kingma and Ba 2015) with learning rate 1e 4, β1 = 0.5, β2 = 0.9 to train both the discriminator and generator. Our gradient penalty coefficient λ (Gulrajani et al. 2017) is set to 10. For our graph construction phase, entities that have more than a 80% match using the generalized Io U metric are considered to be duplicate entities. |