Computer Vision on Ethan Rosenthal

Alignimation: Differentiable, Semantic Image Registration with Kornia

Wed, 03 Nov 2021 00:00:00 +0000

I had a kid at the start of the year.

Hold for applause

Well, not me personally, but my wife did.

I only tell you this in order to tell you that I took a picture of my wife every week that she was pregnant.

We thought maybe it’d be interesting to look back at these pictures one day. She wore the same outfit and faced the same direction for each picture, although the background occasionally changed. In an attempt to be less neurotic about things in my life, I deliberately did not use a tripod or try to get the picture from the exact same angle and distance.

Optimal Peanut Butter and Banana Sandwiches

Tue, 25 Aug 2020 00:00:00 +0000

I was personally useless for most of the Spring of 2020. There was a period of time, though, after the peak in coronavirus cases here in NYC and before the onslaught of police violence here in NYC that I managed to scrounge up the motivation to do something other than drink and maniacally refresh my Twitter feed. I set out to work on something completely meaningless. It was almost therapeutic to work on a project with no value of any kind (insert PhD joke here).

Using Keras' Pretrained Neural Networks for Visual Similarity Recommendations

Mon, 05 Dec 2016 00:00:00 +0000

To close out our series on building recommendation models using Sketchfab data, I will venture far from the previous [posts’]({{ ref “/blog/implicit-mf-part-2” >}}) factorization-based methods and instead explore an unsupervised, deep learning-based model. You’ll find that the implementation is fairly simple with remarkably promising results which is almost a smack in the face to all of that effort put in earlier.

We are going to build a model-to-model recommender using thumbnail images of 3D Sketchfab models as our input and the visual similarity between models as our recommendation score. I was inspired to do this after reading Christopher Bonnett’s post on product classification, so we will follow a similar approach.