I tried using a pose detection network and infer pose from output. This allows me to recognise different gestures without retrain the network. But the cost of running a full pose detection network is high, thus doesn’t work on my operating environment (embedding device).

Since I don’t actually have to frequently redefine the posture I wanted to detect, I had an idea that using a modified YOLO network to simultaneously detect person and classified whether the person is raising both her hand.

This works to some extend, however, this approach requires me to collect bunch of data of a person raising her hand and it is quite difficult to collect much data with any degree of varieties.

Is there a way to work around the data problem ? Many thanks.

I just made a job change into data engineering (Hadoop/Pig/Spark/Kafka) and will be learning machine learning on the side. I’m looking to replace my old workstation(laptop) with a new one, but I don’t have enough knowledge to prioritize the specs I will be needing.

I’m not sure how cost effective a (laptop)GPU is for learning Machine learning and maybe getting to deep learning later.

I’m thinking of investing in a Gaming laptop with RTX2060 but I don’t want to go with a gaming laptop if a laptop GPU wont be enough for long term, and I can do the learning on just a good CPU. Also in that case, is something like i7-8565U good enough or do I need to go with 8750H ?

I don’t know if this is the right place to ask, but I couldn’t find anything about online.

Is there any research currently being conducted on getting a synthesizer preset parameters from an audio sample? If not, would you think that It would be too difficult to create something like this?

There’s plenty of data to train on for this kind of application. Splice for example, has thousands of such presets, and the audio sample wouldn’t be too difficult to obtain once download the preset and load it into some synth and export the resulting audio sample.

What kind type of neural network would be best used for this?

Adam King built a fun web interface for OpenAI’s 345M GPT-2 model:

https://talktotransformer.com

To ensure that I don’t feed it with something it already knows, I seeded it with a quote from last week’s Game of Thrones Season 8 Episode 4 (spoiler!):

She’s a girl who walked into a fire with three stones and walked out with three dragons. How could she not believe in destiny?

But I don’t believe she lived. Her story is not all that unusual. Not in other kingdoms. And what is the difference between a dragon and a girl? The dragon is only two stones and her dragon is three.

As the dragon dies and leaves behind him the girl, she has now lost the chance to fulfill her destiny. In the end, she gives up the body, but it did not have wings. Her power dissipates and the girl dies like a leaf on a river, but with the power of a dragon and the dragon’s magic that could have killed her.

That’s what my stories are like. I don’t know when or why something happens, but it always seems to happen within me.

If a character dies early by going through a terrible time, that might be because of their family or close friends, but sometimes, they could be the result of a curse. It’s too common.

Hi,

my name is Florian and I did my Master Thesis on semi-automated data exploration. As a result, I created the edaviz Python library.

I evaluated the library with a German Data Science consultancy, and they were more than 10x faster on a given standard set of data exploration tasks.

Please check out the short demo video and let me know what you think:

https://youtu.be/eYEeYv11YrQ

​

What is your opinion about the library?

How would you improve edaviz?

What do you not like about edaviz?

​

Thank you for your feedback,

Florian

http://ai.stanford.edu/blog/generate-human-object/

Tremendous progress has been made in the last few years in developing advanced virtual reality (VR) and robotics platforms. As the examples above show, these platforms now allow us to experience more immersive virtual worlds, or allow robots to perform challenging locomotion tasks like walking in snow. So, can we soon expect to have robots that can set the dinner table or do our dishes?

Unfortunately, we are not yet there.

To understand why, consider the diversity of interactions in daily human life. We spend almost all of our waking hours performing activities—simple actions like picking up a fruit or more complex ones like cooking a meal. These physical interactions, called human–object interactions, are multi-stepped and governed by physics as well as human goals, customs, and biomechanics. In order to develop more dynamic virtual worlds, and smarter robots, we need to teach machines to capture, understand, and replicate these interactions. The information we need to learn these interactions is already widely available in the form of large video collections (e.g., YouTube, Netflix, Facebook).

In this post, I will describe some first steps we have taken towards learning multi-step human–object interactions from videos. I will discuss two applications of our method: (1) generating plausible and novel human-object interaction animations suitable for VR/AR, (2) enabling robots to react smartly to user behavior and interactions.

…

The computer vision communities are constantly contributing new datasets and unfortunately it’s not easy to track.

I created a site (visualdata.io) to aggregate the latest computer vision datasets in one place with easy-to-use search functionality. Each dataset has been tagged with one or more topics like image classification, object detection etc and you can dynamically filter with keywords. It also adopts an instagram like scrolling interface to show case the example images in the dataset.

Hope the site is helpful to discover datasets for your projects. New datasets are always being added to the site. Please let me know any feedback you may have!