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..
LinkNet: Relational Embedding for Scene Graph
Authors: Sanghyun Woo, Dahun Kim, Donghyeon Cho, In So Kweon
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our final model, Link Net, through extensive ablation studies, demonstrating its efficacy in scene graph generation. |
| Researcher Affiliation | Academia | Sanghyun Woo* EE, KAIST Daejeon, Korea EMAIL Dahun Kim* EE, KAIST Daejeon, Korea EMAIL Donghyeon Cho EE, KAIST Daejeon, Korea EMAIL In So Kweon EE, KAIST Daejeon, Korea EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. It describes the model architecture and mathematical formulations. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | We conduct experiments on Visual Genome benchmark [16]. |
| Dataset Splits | No | The paper mentions using the Visual Genome benchmark but does not explicitly provide the training/test/validation dataset splits or reference a specific standard split with enough detail within the paper itself. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | In practice, we found setting hyper-parameter r as 2 produces best result from our experiment. By default, we set λ1 and λ2 as 1, and thus all the terms are equally weighted. |