Research in machine perception tackles the hard problems of understanding images, sounds, music and video. In recent years, our computers have become much better at such tasks, enabling a variety of new applications such as: content-based search in Google Photos and Image Search, natural handwriting interfaces for Android, optical character recognition for Google Drive documents, and recommendation systems that understand music and YouTube videos. Our approach is driven by algorithms that benefit from processing very large, partially-labeled datasets using parallel computing clusters. A good example is our recent work on object recognition using a novel deep convolutional neural network architecture known as Inception that achieves state-of-the-art results on academic benchmarks and allows users to easily search through their large collection of Google Photos. The ability to mine meaningful information from multimedia is broadly applied throughout Google.
Recent publications
Bubble patterns recognition using neural networks: Application to the analysis of a two-phase bubbly jet
International Journal of Multiphase Flow, vol. 126 (2020)
Towards a Semantic Perceptual Image Metric
2018 IEEE International Conference on Image Processing
MeteoVis: Visualizing Meteorological Events in Virtual Reality
Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, ACM, Honolulu, Hawaii, pp. 9 (to appear)
Single Image Portrait Relighting
SIGGRAPH (2019)
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