
## Dual Attention Networks for Few-Shot Fine-Grained Recognition
Shu-Lin Xu, Faen Zhang, Xiu-Shen Wei, Jianhua Wang
Keywords: Computer Vision (CV), Machine Learning (ML)
AAAI/2022/Proceedings/20196 - Dual Attention Networks for Few-Shot Fine-Grained Recognition.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?_

[10]

No real details are given regarding their implementation. An overall framework design is given in the paper.

### 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?_

[1]

(3/3)

The authors use three public benchmark data sets, provide key statistics and citations on them. 

### 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?_

[4]

The authors state the hyperparameter values in the experiments section. However with the absence of the implementation its hard to determine if all needed values are present. There is no clarification on how these values were acquired. 

### 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]

The authors present and cite their method to divide the data into training and testing. I takes a bit of subfield experience to determine how many times the experiment was repeated, but they do clearly state in the experiments section. The metrics are straightforward.

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

[2]

Requires a level of expertise regarding the subfield, as certain methods/terminology are quite specific and are cited rather than explained. 
