
## Image Captioning as an Assistive Technology: Lessons Learned from VizWiz 2020 Challenge
Pierre  Dognin, Igor Melnyk, Youssef Mroueh, Inkit Padhi, Mattia  Rigotti, Jarret  Ross, Yair  Schiff, Richard A. Young, Brian  Belgodere
Keywords: 
JAIR/2022/Proceedings/13113 - Image Captioning as an Assistive Technology: Lessons Learned from VizWiz 2020 Challenge.pdf
Project URL: 

### 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]

The authors provide a link to the vizwiz in section 7 (https://github.com/IBM/IBM_VizWiz) but the code is not found.

### 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?_

[2]

(1/1)

The authors use the VizWiz cvaptions dataset, describe it birefly in 5.1 and provide high level statistics in table 1. The data set is linked in their github repo of footnote 7, where more details can be found.

### 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 hyperparmaeter values informally in 5.2. No structured overview or acquisition.

### 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 state the evaluation metrics in 5.2. The data split ist static in the dataset anddescribed in table 1. Results are single model.

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

[8]

Requires expertise in CV.
