Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Unbiased Heterogeneous Scene Graph Generation with Relation-Aware Message Passing Neural Network
Authors: Kanghoon Yoon, Kibum Kim, Jinyoung Moon, Chanyoung Park
AAAI 2023 | Venue PDF | 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. |