For AI2s Charades Challenge, visual systems had to recognize and classify a wide variety of daily activities in realistic videos. This is just a sampling of the videos. (AI2 Photos) <\/p>\n
Some of the worlds top researchers in AI have proved their mettle by taking top honors in three challenges posed by the Seattle-based Allen Institute for Artificial Intelligence. <\/p>\n
The institute, also known as AI2, was created by Microsoft co-founder Paul Allen in 2014 to blaze new trails in the field of artificial intelligence. One of AI2sprevious challenges tested the ability of AI platforms to answer eighth-grade-level science questions. <\/p>\n
The three latest challenges focused on visual understanding that is, the ability of a computer program to navigate real-world environments and situations using synthetic vision and machine learning. <\/p>\n
These arent merely academic exercises: Visual understanding is a must-have for AI applications ranging from self-driving cars to automated security monitoring to sociable robots. <\/p>\n
More than a dozen teams signed up for the competitions, and the algorithms were judged based on their accuracy. Here are the three challenges and the results: <\/p>\n
Charades Activity Challenge: Computer vision algorithms looked at videos of people performing everyday activities for example, drinking coffee, putting on shoes while sitting in a chair, or snuggling with a blanked on a couch while watching something on a laptop. One of the algorithms objectives were to classify all activity categories for a given video, even if two activities were happening at the same time. Another objective was to identify the time frames for all activities in a video. <\/p>\n
Team Kinetics from Google DeepMind won the challenge on both counts. In a statement, AI2 said the challenge significantly raised state-of-the-art accuracy for human activity recognition. <\/p>\n
THOR Challenge: The teams computer vision systems had to navigate through 30 nearly photorealistic virtual scenes of living rooms and kitchens to find a specified target object, such as a fork or an apple, based solely on visual input. <\/p>\n
THORs top finisher was a team from National Tsing Hua University in Taiwan. <\/p>\n
Textbook Question Answering Challenge: Computer algorithms were given a data set of textual and graphic information from a middle-school science curriculum, and then were asked to answer more than 26,000 questions about the content. <\/p>\n
AI2 said the competition was exceptionally close, but the algorithm created by Monica Haurilet and Ziad Al-Halah from Germanys Karlsruhe Institute of Technology came out on top for text questions. Yi Tay and Anthony Luu from Nanyang Technological University in Singapore won the diagram-question challenge. <\/p>\n
The challenge participants significantly improved state-of-the-art performance on TQAs text questions, while at the same time confirming the difficulty machine learning methods have answering questions posed with a diagram, AI2 said. <\/p>\n
The top test scores are pretty good for an AI. But theyd be failing grades for a flesh-and-blood middle-schooler: 42 percent accuracy on the text-question exam, and 32 percent on the diagram-question test. <\/p>\n
Representatives from the winning teams will join other AI researchers at a workshop planned for Wednesday during the 2017 Conference on Computer Vision and Pattern Recognition in Honolulu. <\/p>\n
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AI2 lists top artificial intelligence systems in its Visual Understanding Challenge - GeekWire<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" For AI2s Charades Challenge, visual systems had to recognize and classify a wide variety of daily activities in realistic videos. Continue reading