The amount of imagery that’s collected and disseminated has increased by orders of magnitude over the past couple of years. Deep learning has been instrumental in efficiently extracting and deriving meaningful insights from these massive amounts of imagery. Last October, we released pre-trained geospatial deep learning models, making deep learning more approachable and accessible to a wide spectrum of users.
These models have been pre-trained by Esri on large volumes of data, and can be used as-is, or further fine tuned to your local geography, objects of interest or type of imagery. You no longer need huge volumes of training data and imagery, massive compute resources, or the expertise to train such models yourself. With the pre-trained models, you can bring in the raw data or imagery and extract geographical features at the click of a button. Read More
Daily Archives: July 20, 2021
Scientists adopt deep learning for multi-object tracking
Their novel framework achieves state-of-the-art performance without sacrificing efficiency in public surveillance tasks
Implementing algorithms that can simultaneously track multiple objects is essential to unlock many applications, from autonomous driving to advanced public surveillance. However, it is difficult for computers to discriminate between detected objects based on their appearance. Now, researchers at the Gwangju Institute of Science and Technology (GIST) have adapted deep learning techniques in a multi-object tracking framework, overcoming short-term occlusion and achieving remarkable performance without sacrificing computational speed. Read More
Read the Paper
Google’s Supermodel: DeepMind Perceiver is a step on the road to an AI machine that could process anything and everything
The Perceiver is kind-of a way-station on the way to what Google AI lead Jeff Dean has described as one model that could handle any task, and “learn” faster, with less data.
Arguably one of the premiere events that has brought AI to popular attention in recent years was the invention of the Transformer by Ashish Vaswani and colleagues at Google in 2017. The Transformer led to lots of language programs such as Google’s BERT and OpenAI’s GPT-3 that have been able to produce surprisingly human-seeming sentences, giving the impression machines can write like a person.
Now, scientists at DeepMind in the U.K., which is owned by Google, want to take the benefits of the Transformer beyond text, to let it revolutionize other material including images, sounds and video, and spatial data of the kind a car records with LiDAR.
The Perceiver, unveiled this week by DeepMind in a paper posted on arXiv, adapts the Transformer with some tweaks to let it consume all those types of input, and to perform on the various tasks, such as image recognition, for which separate kinds of neural networks are usually developed. Read More
When Will China Rule the World? Maybe Never
When will China overtake the U.S. to become the world’s biggest economy?
Few questions are more consequential, whether it’s for executives wondering where long-term profits will come from, investors weighing the dollar’s status as global reserve currency, or generals strategizing over geopolitical flashpoints.
In Beijing, where they’ve just been celebrating the 100th anniversary of the Chinese Communist Party, leaders are doing their best to present the baton-change as imminent and inevitable. “The Chinese nation,” President Xi Jinping said last week, “is marching towards a great rejuvenation at an unstoppable pace.” Read More
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