News/Updates

  • 10 April 2021
    Truely Sparse Object Detection: Sparse R-CNN
    A purely sparse method for object detection in images. A fixed set of learned object proposals, total length of N, are used, thus eleiminating upto hundreds of thousands had designed candidate proposals.
  • 5 April 2021
    UPDATE: A more reactive site and blog with gatsby
    Updated this blog site to use gatsby! Allows me to add many react components in pages and provide a more interactive experience.
  • 23 January 2021
    How do you measure trust in deep learning?
    Whether it’s diagnosing patients or driving cars, we want to know whether we can trust a person before assigning them a sensitive task. In the human world, we have different ways to establish and measure trustworthiness.
  • 4 January 2021
    Data-efficient image Transformers
    Image classification — the task of understanding the main content of an image — is easy for humans but hard for machines. In particular, it is challenging for convolution-free Transformers like DeiT...
  • 3 January 2021
    End-to-end object detection with Transformers
    Traditional computer vision models typically use a complex, partly handcrafted pipeline that relies on custom layers in order to localize objects in an image and then extract features.
  • 7 August 2020
    The Surprising Effectiveness of Linear Unsupervised Image-to-Image Translation
    Unsupervised image-to-image translation is an inherently ill-posed problem. This paper uses linear encoder-decoder architectures for unsupervised image to image translation.
  • 2 August 2020
    Robust Training with Ensemble Consensus
    Since deep neural networks are over-parameterized, they can memorize noisy examples. We address such memorizing issue in the presence of annotation noise.
  • 1 August 2020
    BorderDet: Border Feature for Dense Object Detection
    Dense object detectors rely on the sliding-window paradigm that predicts the object over a regular grid of image. Meanwhile, the feature maps on the point of the grid are adopted to generate…
  • 17 May 2020
    Combatting Bias in Medical AI Systems
    Those of us who see the great potential of artificial intelligence in radiology are eager to assure that AI systems work to the benefit of all of our patients...

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