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Details of Grant 

EPSRC Reference: EP/T004991/1
Title: UMPIRE: United Model for the Perception of Interactions in visuoauditory REcognition
Principal Investigator: Damen, Dr D
Other Investigators:
Researcher Co-Investigators:
Project Partners:
nVIDIA Samsung Ultrahaptics Ltd
Department: Computer Science
Organisation: University of Bristol
Scheme: EPSRC Fellowship
Starts: 01 January 2020 Ends: 31 December 2024 Value (£): 1,001,838
EPSRC Research Topic Classifications:
Artificial Intelligence Human Communication in ICT
Image & Vision Computing Vision & Senses - ICT appl.
EPSRC Industrial Sector Classifications:
Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
10 Jul 2019 EPSRC ICT Prioritisation Panel July 2019 Announced
10 Sep 2019 ICT and DE Fellowship Interviews 10 and 11 September 2019 Announced
Summary on Grant Application Form
Humans interact with tens of objects daily, at home (e.g. cooking/cleaning) or outdoors (e.g. ticket machines/shopping bags), during working (e.g. assembly/machinery) or leisure hours (e.g. playing/sports), individually or collaboratively. When observing people interacting with objects, our vision assisted by the sense of hearing is the main tool to perceive these interactions. Let's take the example of boiling water from a kettle. We observe the actor press a button, wait and hear the water boil and the kettle's light go off before water is used for, say, preparing tea. The perception process is formed from understanding intentional interactions (called ideomotor actions) as well as reactive actions to dynamic stimuli in the environment (referred to as sensormotor actions). As observers, we understand and can ultimately replicate such interactions using our sensory input, along with our underlying complex cognitive processes of event perception. Evidence in behavioural sciences demonstrates that these human cognitive processes are highly modularised, and these modules collaborate to achieve our outstanding human-level perception.

However, current approaches in artificial intelligence are lacking in their modularity and accordingly their capabilities. To achieve human-level perception of object interactions, including online perception when the interaction results in mistakes (e.g. water is spilled) or risks (e.g. boiling water is spilled), this fellowship focuses on informing computer vision and machine learning models, including deep learning architectures, from well-studied cognitive behavioural frameworks.

Deep learning architectures have achieved superior performance, compared to their hand-crafted predecessors, on video-level classification, however their performance on fine-grained understanding within the video remains modest. Current models are easily fooled by similar motions or incomplete actions, as shown by recent research. This fellowship focuses on empowering these models through modularisation, a principle proven since the 50s in Fodor's Modularity of the Mind, and frequently studied by cognitive psychologists in controlled lab environments. Modularity of high-level perception, along with the power of deep learning architectures, will bring a new understanding to videos analysis previously unexplored.

The targeted perception, of daily and rare object interactions, will lay the foundations for applications including assistive technologies using wearable computing, and robot imitation learning. We will work closely with three industrial partners to pave potential knowledge transfer paths to applications.

Additionally, the fellowship will actively engage international researchers through workshops, benchmarks and public challenges on large datasets, to encourage other researchers to address problems related to fine-grained perception in video understanding.
Key Findings
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Organisation Website: http://www.bris.ac.uk