Recently I have twice beaten XCOM in the hardest mode — Impossible Ironman. It took me a lot of time to learn things, which helped me to beat the game. Ironically, most of the things I learned are not related to the game. Are you ready? Let’s go.

In case if you are not familiar with XCOM — it is a modern reboot of the classical tactical game from 1993, where you command a small number of soldiers in order to protect the Earth from the Alien Invasion. “Ironman” mode means that you play with a single save, which is…


Let’s speak about ethics.

Recently we have submitted our position paper “ArXiving Before Submission Helps Everyone” to NeurIPS 2020 Workshop on “Navigating the Broader Impacts of AI Research” (NBIAIR).

We had two motivations for the submission.

1. The main motivation was to continue the discussion, which started with our two blog posts, followed by the arXiv paper discussed on twitter and reddit.

2. The secondary — to get meaningful feedback from the reviewers and possibly correct our position and improve our arguments based on that feedback.

The NBIAIR call for papers also specified that submissions may “include case studies, surveys…


Benefits to authors of non-anonymous preprints.

Written by Dmytro Mishkin and Amy Tabb

One of the most popular reactions to our previous post was: “we generally agree, so let’s allow preprints before acceptance, but in anonymous form only.” The current post addresses this idea, and to do it properly, we need to go back to the essentials and try to answer these questions: “What is a scientific publication for?,” “What are the functions of peer review?” and “How is it related to science?” Prepare yourself for a long read.

Content.

Part 1.

  • Functions of conferences.
  • Peer review quality.
  • Conference prefiltering.
  • Role…

Introduction

Recently, we have seen renewed calls that conference submissions should not be posted on arXiv prior to acceptance. The main arguments of these calls are the following:

  • Why can’t you wait 3–6 months?
  • Double blind review becomes a single blind review, which reduces diversity and by some studies have shown to favour people from more famous institutions.

We will show that such arguments are often wrong, and banning pre-acceptance arXiv preprints is harmful especially for early career researchers (abbreviated as ECRs for the remainder of this document). …


По гарячих слідах після CVPR2020 збираю до купи думки про віртуальні наукові конференції.

Чи дорівнює віртуальна конфа реальній, чи ні? Давайте розбиратися.

CVPR складається з 3х складових — воркшопів, туторіалів та власне основної конференції.

Основна конференція — це презентація наукових результатів (статей) у вигляді постерів та доповідей.

Постер-сесія — це десь сотня постерів та пришпилених до них на дві-три години авторів, які чіпляються до всіх перехожих з питанням “Хочете гайд?”.

А глядачі — тобто всі відвідувачі та автори, які не презентують прямо зараз, ходять та роздивляються найкрасивіші та найцікавіші плакати.

В процесі постер-сесії найбільші шанси натрапити на когось знайомого з…


Кількість публікацій в галузі штучного інтелекту (ШІ) така, що ніхто не встигає пропрацювати всі. Чи не здається мені, що це якось неправильно?
Ні, не здається. Поясню чому.

Почну з того, що це — невідворотній процес у будь-якій галузі. Час енциклопедистів проходить, настає час все більш і більш вузьких спеціалістів. Давно немає спеціалістів з “комп’ютерного зору” взагалі, а є — з 3D реконструкції, трекінгу, сегментації, тощо. Тут нічого не вдієш. Але можна доволі швидко перекваліфікуватися до будь-якого іншого напрямку. До будь-якого конкретно, але не до всіх разом.

Ідемо далі. Чи треба пропрацьовувати всі-всі-всі статті? Деякі можна поверхнево глянути і відразу ж…


I have been criticizing whiteboard interviews for a while. Now it is time to tell, how we approached them in our startup, Clear Research.

First, setup.

We were based in Kyiv, Ukraine, in 2014. There are lots of talented engineers there, but no university was teaching computer vision or machine learning at the time. (Now things are changing). Neither computer vision projects were widely done in other companies (this has already changed as well). Nor had we financial resources to hire super-senior developers.

Yet, I need to quickly gather a small, but talented team of research engineers to iterate quickly…


Let`s continue to study the nuances of image correspondences finding. Last time we have covered SIFT implementations. Now I`ll show you how to improve the matching part of the process for free (or almost for free).

The general pipeline of finding image correspondences.

Recap: local features are detected and local image regions around them are embedded to a vector of floats (SIFT, HardNet, etc). Then L2 aka Euclidean distance between each pair is calculated and the keypoint in the second image, which has the smallest descriptor distance is called a tentative match.


The best research is when you are answering questions that bother you and it happens to be in line with your company/grant proposal goals. However, the single core message depending on the way it is written and accompanied artifacts can influence different people, shared across different communities, live long or die fast.

How to be more useful for people and gain more citations?

General things

  • Is it easy to grasp the key message fast and present it on the reading group? If no, you are in trouble.
  • Upload to arXiv. You might be against uploading during the reviewing process, but there is…


The general pipeline of finding image correspondences.

I am starting a series of posts about local image features. It is assumed that you know what they are in general and probably use them in your work. The posts would be about some nuances and details which often are overlooked.

If you are not familiar with local features, I suggest starting from here and here.

In their turn, detector and descriptor can be summed up as following:

  1. local feature detector (DoG (used in SIFT), MSER, Hessian-Affine, FAST) finds some repeatable structures and scale.
  2. Then, usually by some heuristic, local oriented (circular/affine/etc) region to be described is selected.
  3. Then…

Dmytro Mishkin

Computer Vision researcher and consultant. Co-founder of Ukrainian Research group “Szkocka”.

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