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kalal's comments

kalal | 5 years ago | on: Why I’m a sucker for pen and paper

I personally use more or less 3 stages. 1. whiteboard and marker - explore ideas, big-picture thinking, fast iterations. 2. paper notebook - things that emerged from point 1 are more precisely recorded into a paper notebook. Things go much slower here, especially because the eraser or the paper burns out quite quickly and there is no point to keep pointless notes. The structure of the paper is quite important as well. I noticed blank paper does not work that well and I spent more space there with less structure. Linked paper is also not good. Square paper is better. But the best one for me is dotted paper. Ideal compromise between structure and freedom. 3. everything that survives stage 2 goes to real code and git logs. Here the structure is maximal. Keeping notes in the code is of course good way as well as extensive git comments.

kalal | 5 years ago | on: Problems with AI-based monitoring startups (2018)

Does the tone bothers anybody? After reading first couple of sentences I got the impression that the author knows it all and everybody else is just stupid... This may be cultural difference, or too much sensitivity on my side. I don't know.

kalal | 5 years ago | on: Building a simple neural net in Java

I am afraid that a decision tree HAS direct impact on the forest. Multiple trees averaged give the forest response. It's like saying, one vote in elections has nothing to do with the final decision.

Sure we have heard it many times that it IS a like a brain, and many times it is NOT like a brain. It really depends on your intended audience, but it is not worth picking on and it feels like the tabs/spaces problem. BTW: tabs of course!

kalal | 5 years ago | on: Label Drivers Like in a Game

MOTO AI is a project supported by EPIC MegaGrant. Our mission is to transform motorsport viewing experience. Our first use case is to augment the race with driver labels. This is achieved by 3D tracking of the cars in real time.

kalal | 7 years ago | on: Ask HN: Who is hiring? (November 2018)

TLD Vision | Czech Republic | ONSITE | http://tldvision.com

TLD Vision an AI company focusing on tracking of objects in video. We are building a new tracking engine which combines latest advances from deep neural networks, real-time rendering and 3D computer vision. Our goal is to enable new kinds of applications in broadcasting and augmented reality which require extreme robustness and precision.

The following positions are currently available: C++ developer, 3D graphics developer, Visual tracking researcher, Deep learning researcher, Android developer

Apply here: [email protected]

kalal | 7 years ago | on: What You Need to Know Before Considering a PhD

To me, this is whether you have good people around or not. PhD usually comes with good advisers who help you think in different ways than you have done before. However, such good advisers can be also at a company + you work on practical an modern problems (usually). For that you don't need PhD. In any case, once you ask whether you should do PhD or not it feels like you don't want to be left out. In that case, just do it.

kalal | 8 years ago | on: Deep Painterly Harmonization

Consider for example a picture of a street and a rendered car overlaying it. It would be interesting to see how well the technology bakes in the car so that it matches the environment. This would go in direction of realistic rendering.

kalal | 8 years ago | on: Tesla crash in September showed similarities to fatal Mountain View accident

When the self driving revolution started, conservative people claimed that one accident would stop the whole business. Well there are many accidents already and people are still willing to risk. One reason may be our tendency to trust something which we do not understand. We feel that if the feature is already in the car, it must be safe. Well it is not. And even worse, it is driven more by marketing and money than by safety regulations.

kalal | 8 years ago | on: How to Easily Detect Objects with Deep Learning on Raspberry Pi

I am very skeptical about pretraining which seems to be the key point of Nanonets. Sure, it will help to work work better than initialization from random weights, but, you will always do better if you collect more data for your problem. This may be fine for problems which do not need optimal classification and fast performance, but I am struggling to see any use case for that.

kalal | 8 years ago | on: Volume.gl – 3D Model from Single Image

Can you link to the images you have tried? My tests were rather disappointing. Yes, I got some 3D point cloud from the image, but nothing that I would consider useful outside computer vision community for now.

kalal | 8 years ago | on: Live Computer Vision Coding

You are right, there is good application for this tool in education or people who want to quickly test something new. Shadertoy is great and I like it. It is at the right level of abstraction that I need for my current CG knowledge. I can imagine that there are many people who find Live CV very useful and then move downwards.

kalal | 8 years ago | on: Live Computer Vision Coding

Prototyping YOLO or the other DNN means just run detector and see the boxes. Of course training these systems is clearly out of reach for this kind of abstraction technology, not to mention modify and develop something new.
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