Kiana Ehsani
PhD Student in Computer Science
University of Washington

About Me

I'm a PhD student at University of Washington in the Paul G. Allen School of Computer Science & Engineering. Currently, I am working as a research assistant under supervision of professor Ali Farhadi.

My research interest is in computer vision and machine learning. In my current projects, I'm mainly using deep learning and Convolutional Neural Networks.



Here are some of the research projects I have been working on.

SeGAN: Segmenting and Generating the Invisible

K Ehsani, R Mottaghi, A Farhadi (CVPR18, spotlight)

Humans have strong ability to make inferences about the appearance of the invisible and occluded parts of scenes. For example, when we look at the scene on the left we can make predictions about what is behind the coffee table, and can even complete the sofa based on the visible parts of the sofa, the coffee table, and what we know in general about sofas and coffee tables and how they occlude each other.

SeGAN can learn to
  1. Generate the appearance of the occluded parts of objects,
  2. Segment the invisible parts of objects,
  3. Although trained on synthetic photo realistic images reliably segment natural images,
  4. By reasoning about occluder-occludee relations infer depth layering.

PDF Project Page Presentation

Who Let The Dogs Out? Modeling Dog Behavior From Visual Data

K.Ehsani, H.Bagherinezhad, J. Redmon, R. Mottaghi, A. Farhadi (CVPR18)

Dogs are intelligent. Let's learn from them! We introduce DECADE, a dataset of ego-centric videos from a dog’s perspective as well as her corresponding movements.

In this paper we propose a model that can
  1. Learn to act like a dog (Predict the dog’s future moves),
  2. Learn to plan like a dog (Estimate a sequence of movements that take the state of the dog’s world from what is observed at a given time to a desired observed state),
  3. Learn from a dog (Exploring the potentials of using the dog's movements for representation learning),

PDF Project Page

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