
## GenRec: Unifying Video Generation and Recognition with Diffusion Models
Zejia Weng, Xitong Yang, Zhen Xing, Zuxuan Wu, Yu-Gang Jiang
Keywords: 
NeurIPS/2024/Proceedings/94684 - GenRec: Unifying Video Generation and Recognition with Diffusion Models.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 their implementation (https://github.com/wengzejia1/GenRec). Repo is empty. No other details given.

### 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?_

[3]

(4/4)

The authors use four data sets stated in 4.1. and provide citations on each. Every data set has a description with high level statistics, but more information would be welcome. No direct links given.

### 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]

The authors state the hp values in 4.1. under implementation details. No acquisition method specified.

### 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?_

[3]

The authors state the evaluation protocol in 4.1. The metric is not standard but a citation is provided and it is explained. Top-1 accuracy is standard. The results are single model. Train test split is not clear.

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

[6]

Requires expertise on diffusion models, CV for video generation and recognition.
