About the Exam
The format of the exam is a mock peer-reviewed conference. You will develop an algorithm, prepare a professional paper, submit an anonymized version to the peer review system (details about this to be provided soon!), and peer-review the work from other groups. There are three deliverables:

- **Phase 1: Develop your algorithm and write your writeup**
   - Develop your algorithm and submit your results to the Kaggle leaderboard. Deadline: **Monday, Dec 9, 11:59 PM EST.**
   - Write your paper submission and submit to CMS and the peer review system. Details about the paper review system will be provided soon. Deadline: **Monday, Dec 9, 11:59 PM EST**. There should be only one submission from each team.
- **Phase 2: Peer-review other groups' work!**
   - Write reviews and submit them to CMS and the peer review system. Deadline: **Wednesday, Dec 11, 11:59 PM EST**. (Will open after Phase 1 ends)
   - Each team will be responsible for completing two reviews. If you are working in a team of two you may separately complete one review each. However if you are working individually you must complete both reviews yourself.
The Challenge: Build a large-scale image search engine!
You and your team of **two Cornell Tech students** are surely on the path to fame and fortune! You have been recruited by Google to disrupt Google Image Search by building a better search engine using novel statistical learning techniques.

The specifications are simple: We need a way to **search for relevant images** given a natural language query. For instance, if a user types "dog jumping to catch frisbee," your system will **rank-order the most relevant images** from a large database.

Here are some details:

- **During training**, you have a dataset of 10,000 samples. Each sample has the following data available for learning:
   - A 224x224 JPG image.
   - A list of tags indicating objects appeared in the image.
   - Feature vectors extracted using [ResNet](https://arxiv.org/abs/1512.03385), a state-of-the-art Deep-learned CNN (You don't have to train or run ResNet -- we are providing the features for you). See [here](http://ethereon.github.io/netscope/#/gist/b21e2aae116dc1ac7b50) for the illustration of the ResNet-101 architecture. The features are extracted from pool5 and fc1000 layer.
   - A five-sentence description, used to train your search engine.
- **During testing**, your system matches a single five-sentence description against a pool of 2,000 candidate samples from the test set. Each sample has:
   - A 224x224 JPEG image.
   - A list of tags for that image.
   - ResNet feature vectors for that image.
- **Output**: For each description, your system must **rank-score** each testing image with the likelihood of that image matches the given sentence. Your system then returns the name of the top 20 relevant images, delimited by space. See "sample_submission.csv" on the data page for more details on the output format.
- **Evaluation metric**: There are 2,000 descriptions, and for each description, you must compare against the entire 2,000-image test set. That is, rank-order test images for each test description. We will use **MAP@20** as the evaluation metric. If the corresponding image of a description is among your algorithm's 20 highest scoring images, this metric gives you a certain score based on the ranking of the corresponding image. Please refer to the evaluation page for more details. 

Use all of your skills, tools, and experience. It is OK to use libraries like numpy, scikit-learn, pandas, etc., as long as you cite them. Use cross-validation on training set to debug your algorithm. Submit your results to the Kaggle leaderboard and send your complete writeup to EasyChair. The data you use --- and the way you use the data --- is completely up to you.

The best teams (of **two Cornell Tech students**, recall) might use visualization techniques for debugging (e.g., show top images retrieved by your algorithm and see whether they make sense or not), preprocessing, a nice way to compare tags and descriptions, leveraging visual features and combining them with tags and descriptions, supervised and/or unsupervised learning to best understand how to best take advantage of each data source available to them.

The best reports will be professionally written. We suggest including an **Abstract**, followed by an **Introduction** section including motivations of your method and a detailed description of each step in your pipeline / framework you used for this task. Then, include a detailed **Experiment** section and a **Results** section. If time permits, a **Background / Related Work** section can help to point out the relevant work in the image retrieval literature.

The best reports use a professional style adopted by a major academic conference. NIPS is a good choice. Teams can download a template from Section 4 Paper Format of the [NIPS Author Guidelines](https://nips.cc/Conferences/2015/PaperInformation/AuthorSubmissionInstructions). We recommend you to use the LaTeX template, but this is not a requirement. If you're more familiar with Word, use Word.

The best peer reviews will be detailed and thorough, pointing out potential areas of improvement as well as complimenting their peers' strengths.

The best teams understand how to divide the precious time and energy among all members of the group. They might split into roles, preventing programming students from messing up the writing and ensuring writing gurus don't screw up the code. Close contact is always maintained within top-performing teams. When hardship happens within the best teams, blame is not assigned---it is overcome.

May the best team win!
Rules
You are strongly encouraged to work in a group of **two** students.

Groups may not collaborate with other groups. You cannot cite other teams' work. This includes sharing data, intermediate and final results, writing, or discussing your detailed method with other groups. You **may not** view reports from other teams until **after** the peer-review phase begins!
How to anonymize your paper
The "Peer Review" phase will be double-blind. This means authors will not know who the reviewers are, and the reviewers should not know who the authors are. This protects both sides.

To maintain anonymity, **don't** include team member names in the actual paper that you submit to Dropbox!

However, we need to know who you are. So please use the following file name: `TeamNameOnKaggle_Author1lastname_Author2lastname_report.pdf` This will be hidden from your reviewers.

![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3729445%2F4dec6303e60a0b5a7363a7ab1eec01d0%2FF255rFT.png?generation=1574711022606483&alt=media)
Peer Review Tips
See the following resources for tips on how to write a professional review:

- [how to peer review](http://matt.might.net/articles/how-to-peer-review/)
- [Peer Fortress: The Scientific Battlefield](http://matt.might.net/articles/peer-fortress/)
- [CVPR 2016 Reviewer’s Guidelines](http://cvpr2016.thecvf.com/)
- [So You're a Program Committee Member Now: On Excellence in Reviews and Meta-Reviews and Championing Submitted Work That Has Merit](http://mobilehci.acm.org/2015/download/ExcellenceInReviewsforHCICommunity.pdf)
Score Breakdown<\h1>
- Rank: 25 points total calculated as:
  - 10 pts for being 25% above baseline
  - 15 pts given as 15pt*your score/(max score - min score)
- Method: 50 points calculated as:
  - 25 points for method quality and reasonableness/innovativeness of approach
  - 25 points for quality of report
- Review: 25 points for doing a reasonable job