LinkNet: Relational Embedding for Scene Graph
Authors: Sanghyun Woo, Dahun Kim, Donghyeon Cho, In So Kweon
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | 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 shwoo93@kaist.ac.kr Dahun Kim* EE, KAIST Daejeon, Korea mcahny@kaist.ac.kr Donghyeon Cho EE, KAIST Daejeon, Korea cdh12242@gmail.com In So Kweon EE, KAIST Daejeon, Korea iskweon@kaist.ac.kr |
| 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. |