Computer vision

ObjectNet, a dataset of photos created by MIT and IBM researchers, shows objects from odd angles, in multiple orientations, and against varied backgrounds to better represent the complexity of 3D objects. The researchers hope the dataset will lead to new computer vision techniques that perform better in real life.

This object-recognition dataset stumped the world’s best computer vision models

Objects are posed in varied positions and shot at odd angles to spur new AI techniques.

December 10, 2019

Based on shadows that an out-of-view video casts on nearby objects, MIT researchers can estimate the contents of the unseen video. In the top row, researchers used this method to recreate visual elements in an out-of-view video; the original elements are shown in the bottom row.

Using computers to view the unseen

A new computational imaging method could change how we view hidden information in scenes.

December 6, 2019

An MIT-invented model demonstrates an understanding of some basic “intuitive physics” by registering “surprise” when objects in simulations move in unexpected ways, such as
rolling behind a wall and not reappearing on the other side.

Helping machines perceive some laws of physics

Model registers “surprise” when objects in a scene do something unexpected, which could be used to build smarter AI.

December 2, 2019

In lane-merging scenarios, a system developed at MIT could distinguish between altruistic and egoistic driving behavior.

Predicting people's driving personalities

System from MIT CSAIL sizes up drivers as selfish or selfless. Could this help self-driving cars navigate in traffic?

November 18, 2019

MIT researchers have developed a trajectory-planning model that helps drones fly more safely at high speeds through previously unexplored areas, which could aid search-and-rescue missions through dense forests.

System prevents speedy drones from crashing in unfamiliar areas

Drones can fly at high speeds to a destination while keeping safe “backup” plans if things go awry.

October 25, 2019

MIT researchers are training a pair of generative adversarial networks, or GANs, to mimic the land, sea, and cloud textures seen in satellite images with the goal of eventually visualizing real-world sea-level rise. It’s one of many artificial intelligence research projects made possible by IBM and Google-donated cloud credits.

What a little more computing power can do

Commercial cloud service providers give artificial intelligence computing at MIT a boost.

September 16, 2019

MIT graduate students Logan Engstrom (left) and Andrew Ilyas have published five papers together exploring the vulnerabilities of computer vision systems, and they have half a dozen more in the pipeline.

Why did my classifier just mistake a turtle for a rifle?

Two longtime friends explore how computer vision systems go awry.

July 31, 2019

The GANpaint system developed at MIT can easily add features to an existing image. At left, the original photo of a kitchen; at right, the same kitchen with the addition of a window. Co-author Jun-Yan Zhu believes better understanding of GANs will help researchers be able to better stamp out fakery: “This understanding may potentially help us detect fake images more easily.”

Teaching artificial intelligence to create visuals with more common sense

An MIT/IBM system could help artists and designers make quick tweaks to visuals while also helping researchers identify “fake” images.

July 1, 2019

Yunzhu Li is a PhD student at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL).

Teaching artificial intelligence to connect senses like vision and touch

MIT CSAIL system can learn to see by touching and feel by seeing, suggesting future where robots can more easily grasp and recognize objects.

June 17, 2019

Inkbit, a startup out of MIT, has created a multimaterial 3-D printer capable of printing extremely flexible materials more accurately than competitors, according to the founders.

A 3-D printer powered by machine vision and artificial intelligence

MIT startup Inkbit is overcoming traditional constraints to 3-D printing by giving its machines “eyes and brains.”

June 4, 2019

Artistic rendering of artificial intelligence and deep learning visual recognition systems

For better deep neural network vision, just add feedback (loops)

The DiCarlo lab finds that a recurrent architecture helps both artificial intelligence and our brains to better identify objects.

April 29, 2019

From an original transparent etching (far right), engineers produced a photograph in the dark (top left), then attempted to reconstruct the object using first a physics-based algorithm (top right), then a trained neural network (bottom left), before combining both the neural network with the physics-based algorithm to produce the clearest, most accurate reproduction (bottom right) of the original ...

Deep-learning technique reveals “invisible” objects in the dark

Method could illuminate features of biological tissues in low-exposure images.

December 12, 2018

MIT researchers describe an autonomous system for a fleet of drones to collaboratively search under dense forest canopies using only onboard computation and wireless communication — no GPS required.

Fleets of drones could aid searches for lost hikers

System allows drones to cooperatively explore terrain under thick forest canopies where GPS signals are unreliable.

November 1, 2018

MIT researchers have developed a “semantic parser” that learns through observation to more closely mimic a child’s language-acquisition process, which could greatly extend computing’s capabilities.

Machines that learn language more like kids do

Computer model could improve human-machine interaction, provide insight into how children learn language.

October 31, 2018

Recognizing the partially seen

Advances in computer vision inspired by human physiological and anatomical constraints are improving pattern completion in machines.

September 20, 2018

MIT News | Massachusetts Institute of Technology

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