Submodular Field Grammars: Representation, Inference, and Application to Image Parsing
Authors: Abram L. Friesen, Pedro M. Domingos
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we show promising improvements in accuracy when using SFGs for scene understanding, and demonstrate exponential improvements in inference time compared to traditional methods, while returning comparable minima. |
| Researcher Affiliation | Academia | Abram L. Friesen and Pedro Domingos Paul G. Allen School of Computer Science and Engineering University of Washington Seattle, WA 98195 {afriesen,pedrod}@cs.washington.edu |
| Pseudocode | Yes | Algorithm 1 Compute the (approximate) MAP parse of an image with respect to an SFG. |
| Open Source Code | No | The paper mentions supplementary material but does not provide a direct link to source code or explicitly state that source code for the methodology is released. |
| Open Datasets | Yes | The Deep Lab features are trained on the Stanford Background Dataset (SBD) [5] training set. |
| Dataset Splits | No | The paper mentions a 'training set' and 'test set' but does not explicitly describe a validation set or its specific split for reproduction. |
| Hardware Specification | Yes | The DGX-1 used for this research was donated by NVIDIA. |
| Software Dependencies | No | The paper mentions the use of 'Deep Lab features' and algorithms like 'belief propagation' and 'α-expansion' but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | In all MRFs (planar and in each SFG), the pairwise terms are standard contrast-dependent boundary terms [25] multiplied by a single weight, w BF. |