基本信息
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职业迁徙
个人简介
Research
I am interested in problems related to deep learning, computational photography and computer vision.
Deep Learning:
Deep learning has revolutionized applications in CV and ML. I am interested in supervised as well as unsupervised learning algorithms. My recent work in this area includes building deep learning models for semantic segmentation and unsupervised learning algorithm for recommending complementary items.
Context Encoding for Semantic Segmentation, CVPR 2018 (oral presentation)
CRAFT: Complementary Recommendations Using Adversarial Feature Transformer
Computational Photography:
With advances in sensors and computational power, cameras coupled with a computer offer us new possibilities which were not possible with traditional film based cameras. Computational photography is emerging as a new field combining computer vision, graphics and imaging to overcome the limitations of current cameras. Traditional approaches try to combine multiple photos with varying parameters to overcome the limitations of the cameras. However, there is a need for novel sensors for specific applications that go beyond the traditional capturing of the scene as a regular grid of pixel intensities. Such sensors, for example, include coding and modulation strategies along dimensions of space, time, angle and/or wavelength.
I have creatively devised novel solutions for many classic imaging problems like motion blur, narrow depth of field, low frame rate, lens glare, and photo artifacts due to flash and glass reflections. These problems are manifestations of inevitable tradeoffs and loss of visual information in photography. My emphases have been on analyzing the underlying loss of information and modifying the image capture process itself to solve the problem, in contrast to traditional software-only approaches which are often inadequate. In solving these problems, my goal is to design easy, low cost solutions that anyone can build as well as to enhance off-the-shelf imaging devices.
I am interested in problems related to deep learning, computational photography and computer vision.
Deep Learning:
Deep learning has revolutionized applications in CV and ML. I am interested in supervised as well as unsupervised learning algorithms. My recent work in this area includes building deep learning models for semantic segmentation and unsupervised learning algorithm for recommending complementary items.
Context Encoding for Semantic Segmentation, CVPR 2018 (oral presentation)
CRAFT: Complementary Recommendations Using Adversarial Feature Transformer
Computational Photography:
With advances in sensors and computational power, cameras coupled with a computer offer us new possibilities which were not possible with traditional film based cameras. Computational photography is emerging as a new field combining computer vision, graphics and imaging to overcome the limitations of current cameras. Traditional approaches try to combine multiple photos with varying parameters to overcome the limitations of the cameras. However, there is a need for novel sensors for specific applications that go beyond the traditional capturing of the scene as a regular grid of pixel intensities. Such sensors, for example, include coding and modulation strategies along dimensions of space, time, angle and/or wavelength.
I have creatively devised novel solutions for many classic imaging problems like motion blur, narrow depth of field, low frame rate, lens glare, and photo artifacts due to flash and glass reflections. These problems are manifestations of inevitable tradeoffs and loss of visual information in photography. My emphases have been on analyzing the underlying loss of information and modifying the image capture process itself to solve the problem, in contrast to traditional software-only approaches which are often inadequate. In solving these problems, my goal is to design easy, low cost solutions that anyone can build as well as to enhance off-the-shelf imaging devices.
研究兴趣
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International Journal of Computer Visionno. 2 (2013): 123-138
Computer Visionno. 1 (2013): 1009-1016
Computer Vision and Pattern Recognitionno. 1 (2013): 1399-1406
Computer Visionno. 1 (2013): 2368-2375
International Journal of Computer Visionno. 1-3 (2012): 33-55
mag(2010)
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