Unbiased Heterogeneous Scene Graph Generation with Relation-Aware Message Passing Neural Network
Authors: Kanghoon Yoon, Kibum Kim, Jinyoung Moon, Chanyoung Park
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive evaluations demonstrate that Het SGG outperforms state-of-the-art methods, especially outperforming on tail predicate classes. |
| Researcher Affiliation | Academia | 1Dept. of Industrial and Systems Engineering, KAIST, Daejeon, Republic of Korea 2Graduate School of Artificial Intelligence, KAIST, Daejeon, Republic of Korea 3Electronics and Telecommunications Research Institute, 218 Gajeong-ro, Yuseong-gu, Daejeon, Republic of Korea 4 ETRI School, University of Science and Technology, 218 Gajeong-ro, Yuseong-gu, Daejeon, Republic of Korea |
| Pseudocode | No | The paper describes the proposed method using prose and mathematical equations, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code for Het SGG is available at https://github. com/Kanghoon Yoon/hetsgg-torch. |
| Open Datasets | Yes | We evaluate Het SGG compared with state-of-the-arts methods on commonly used benchmark datasets, i.e., Visual Genome (VG) (Krishna et al. 2017), and Open Images (OI) V6 (Kuznetsova et al. 2020b). |
| Dataset Splits | Yes | A total of 108k images are split into training set (70%) and test set (30%)." and "OI V6 has 301 object classes, and 31 predicate classes, and is split into 126,368 train images, 1,813 validation images, and 6,322 test images. |
| Hardware Specification | No | The paper mentions the use of specific models like Res Ne Xt-101-FPN and Faster R-CNN, but does not provide details on the hardware (e.g., specific GPU or CPU models) used for experiments. |
| Software Dependencies | No | The paper does not specify version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | For RMP of Het SGG, we set the number of bases (i.e., b) to 8 in VG and 4 in OI, and use four MPNN layers (i.e., l = 4). For SGGen task, we select the top 80 object proposals sorted by object scores, and use per-class non-maximal suppression (NMS) (Zellers et al. 2018) at Io U 0.5. |