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Found 7 papers, 0 papers with code
FaceTouch: Detecting hand-to-face touch with supervised contrastive learning to assist in tracing infectious disease
Despite partial occlusion of faces, the introduced system learns to detect face touches from the RGB representation of a given scene by utilising the representation of the body gestures such as arm movement.
ImageSig: A signature transform for ultra-lightweight image recognition
With very few parameters and small size models, the key advantage is that one could have many of these "detectors" assembled on the same chip; moreover, the feature acquisition can be performed once and shared between different models of different tasks - further accelerating the process.
Re-designing cities with conditional adversarial networks
This paper introduces a conditional generative adversarial network to redesign a street-level image of urban scenes by generating 1) an urban intervention policy, 2) an attention map that localises where intervention is needed, 3) a high-resolution street-level image (1024 X 1024 or 1536 X1536) after implementing the intervention.
Generative Adversarial Network
Image-to-Image Translation
+2
CyclingNet: Detecting cycling near misses from video streams in complex urban scenes with deep learning
Cycling is a promising sustainable mode for commuting and leisure in cities, however, the fear of getting hit or fall reduces its wide expansion as a commuting mode.
WeatherNet: Recognising weather and visual conditions from street-level images using deep residual learning
Despite the significance of this subject, it is still not been fully addressed by the machine intelligence relying on deep learning and computer vision to detect the multi-labels of weather and visual conditions with a unified method that can be easily used for practice.
URBAN-i: From urban scenes to mapping slums, transport modes, and pedestrians in cities using deep learning and computer vision
How can deep learning and Artificial Intelligence (AI) untangle the complexities of informality to advance urban modelling and our understanding of cities?
predictSLUMS: A new model for identifying and predicting informal settlements and slums in cities from street intersections using machine learning
We applied the model in five major cities in Egypt and India that have spatial structures in which informality is present.
