## Assessing a Single Image in Reference-Guided Image Synthesis
Jiayi Guo, Chaoqun Du, Jiangshan Wang, Huijuan Huang, Pengfei Wan, Gao Huang
Keywords: Computer Vision (CV)
AAAI/2022/Proceedings/19956 - Assessing a Single Image in Reference-Guided Image Synthesis.pdf

### Implementation
_Given the documentation given by the authors on the method, how much time investment would it be to re-implement the method from scratch?_

[7]

Code is not included +4. Frameworks and languages are named, design choices are listed. No other code repositories are listed +1. No pseudocode or architecture figures +2. References to papers with same architecture -1.

### Data
_Given the data description in the documentation, how much effort take to either: Find the same dataset the authors used, or similar datasets and defend the comparability, or acquire one from scratch?_

[4]

Main datasets: No direct link, but citation +1. No description of datasets +1. No statistics +1. No indication of whether data is private or public, but they seem common and public. Human annotation for this paper: No direct link, citation not applicable +1. Description present. Statistics limited to number of samples +1. Authors appear not to make their annotated data public +2. 

### Configuration 
_Given the (hyper)parameters, including semantic parameters, of the method: How much effort would it take to acquire the algorithm configurations used for their results, and compare against their budgetary constraints?_

[3]

Hyperparameters were explained well in text, but not summarised +1. Hyperparameter values are clearly communicated. Hyperparameter acquisition strategy and budget not stated +2.

### Experimental Procedure
_Given the experimental set-up of the work, how difficult is it to set up a new experiment, similar to those presented in the original work, with the same procedure?_

[1]

Metrics are named, explained and cited. Train/val/test split not relevant (authors propose a metric and evaluate agreement with human annotators). Acquisition is explained. Aggregation is clear. Distribution indication not explicitly explained in experiments, but explained in datasets section (illogical ordering, but included). 

### Expertise
_How much effort would it take to acquire the expertise required to reproduce the work independently relying on the available documentation?_

[3]

The components of the proposed metric are separated, and the maths introduced by the authors is not overly complicated. Reproducing the paper would not require too much expertise, including the human annotation.
