Geneviève Patterson

Upcoming Talks & Events

CV & HCI researcher. Collaborator at MBARI's Ocean Vision AI, where we bring together ML and deep sea enthusiasts to find and count all the ocean's animals.

Projects:

IEEE Transactions Latin America: Special Issue on Artificial Intelligence for Sustainability

Sustainability is the challenge of our time. This special issue of IEEE Latin America Transactions highlights efforts that combine AI and Sustainability research in Latin America and the Carribean.

Nov 2022: IEEE Latin America Transactions, co-edited by Joaquin Salas, Genevieve Patterson, and Flavio de Barros Vidal

Kaizen: Human-in-the-Loop Active Learning

The github repo for this project has been updated to support Python3.9 and Flask >2.1. Look for future updates!

The experimental details for this project are in the paper Kaizen: The Crowd Pathologist (extension of the Tropel project), originally developed at Microsoft Research New England | 2016, re-release 2022 | Lam, Patterson

Nov 3, 2016: HCOMP 2016, Groupsight Workshop

Practical Machine Learning Tools for Climate Impact

This is an intro to ML for non-ML people. Especially people interested in doing climate related work. This version of the course was presented at the LEADS summer school at Concordia University in May 2022

Developed at CCAI | 2021-2022 | Patterson, Kuntz

DeepMiner: Discovering Interpretable Representations for Mammogram Classification and Explanation (extension of previous work)

at Microsoft Research New England | 2021 | Wu, Zhou, Peck, Hsieh, Dialani, Mackey, Patterson

Oct 28, 2021: HDSR 2021

When Content Decides Where You Belong: Investigating Micro Communities at VSCO

at VSCO | 2021 | Mohanty, Patterson

Nov 14-18, 2021: HCOMP 2021

The TRASH App for iOS (my startup)

This app was my baby. App of the Day March 2020 and a creative outlet for a hundred thousand video artists <3. Our app store description read,

Make amazing videos in seconds. Weekend recaps, weird edits, music videos, montages, travel vids, supercuts of your stories - TRASH is instant video for iOS. Choose a few clips from your camera roll, and TRASH will help you edit them together with "cinema science."
2018-2020. Acquired by VSCO. RIP.

Humor in Word Embeddings: Cockamamie Gobbledegook for Nincompoops

at Microsoft Research New England | 2019 | Gultchin, Patterson, Baym, Swinger, Kalai

May 24, 2019: ICML 2019

DeepMiner: Discovering Interpretable Representations for Mammogram Classification and Explanation

at Microsoft Research New England | 2019 | Wu, Zhou, Peck, Hsieh, Dailani, Mackey, Patterson

Code available on GitHub

The Trouble With Trusting AI to Interpret Police Body-Cam Video

at Microsoft Research New England | 21 Nov 2018 | Greene and Patterson

IEEE Spectrum

Crowd-Guided Ensembles: How Can We Choreograph Crowd Workers for Video Segmentation?

at Computational Fabrication Group, MIT | 2018 | Kaspar, Patterson, Kim, Aksoy, Matusik, Elgharib

April 21 - 26, 2018: CHI 2018

Expert identification of visual primitives used by CNNs during mammogram classification

at Microsoft Research New England | 2018 | Wu, Peck, Hsieh, Dailani, Lehman, Zhou, Syrgkanis, Mackey, Patterson

February 10 - 15, 2018: SPIE Medical Imaging

RNNbow: Visualizing Learning via Backpropagation Gradients in Recurrent Neural Networks

at Tufts University | 2017 | Cashman, Patterson, Mosca, Chang

VADL 2017: Workshop on Visual Analytics for Deep Learning, Best Paper
Please contact Dylan Cashman @ Tufts for more info about using the RNNbow tool.

COCO Attributes

at Hays Lab | 2016 | Patterson, Hays

COCO Attributes Dataset v1 Released
3.5 million (object, attribute) annotation pairs for 180,000 objects. Our ECCV 2016 paper. [ project webpage ]
Github Repository.

Tropel: Crowdsourcing Detectors with Minimal Training

at Hays Lab | 2015 | Patterson, Van Horn, Belongie, Perona, Hays

Creating detectors for arbitrary visual events using only one example and a crowd workforce. HCOMP 2015 Best Paper Finalist.

Scene Attributes Journal Version

at Hays Lab | 2014 | Patterson, Xu, Su, Hays

In this expansion of the original SUN Attributes paper, we demonstrate the usefulness of attributes for scene classification, scene parsing, image captioning and image similarity search. May 2014 IJCV Paper.

Crowd in the loop Active Learning

at Hays Lab | 2013 | Patterson, Van Horn, Belongie, Perona, Hays

In this paper we present a method for bootstrapping classifiers for fine-grained visual classes using minimal labeled data (5-10 positive examples per class). NIPS 2013 Crowd Workshop paper and the poster.

Using humans to build mid-level features

at Hays Lab | 2013 | Patterson, Lin, Hays

We introduce a crowd-in-the-loop method for unsupervised discovery of mid-level patches. We demonstrate this feature discovery process on the 15 scene dataset and compare to methods that do not use crowdsourcing. CVPR 2013 Workshop Paper and the poster.

Basic Level Scene Understanding

at Hays Lab | 2013 | Xiao, Hays, Russell, Patterson, Ehinger, Torralba, Oliva

This paper provides an overview of many methods of scene understanding explored using the SUN dataset. Frontiers in Psychology Paper.

Scene Attributes

at Hays Lab | 2012 | Patterson, Hays

Attribute Classifiers v2 Released Per image, attributes now classified in ~12sec. Please check the project webpage.
Github Repository.

We present the first large-scale scene attribute database. Our attribute database spans more than 700 categories and 14,000 images. Our CVPR 2012 paper. [ project webpage ]

Gathering Attributes on Amazon Mechanical Turk

at Hays Lab | 2011 | Patterson, Hays

For the SUN Attribute dataset project, I worked to build a reliable Turker workforce to label the dataset. A summary of this experience is available in the CVPR 2011-FGCV Workshop paper

CVPR 2011 Fine-Grained Computer Vision Workshop paper and poster.

Masters Research

Transverse Flux Motor for Direct Drive Ship Propulsion

at the Koseki Lab | October 2007 - October 2009

During my Masters' course I was interested in novel topologies for permanent magnet motors. My general interest was the design and control of electrical machines and electro-magnetic phenomena for transportation (trains, ships, submarines). Please check the award winning ICEMS2009 poster and the paper.

Datasets:

COCO Attributes Dataset

at Hays Lab | 2016

To download this dataset, please visit the project webpage. The image on the left is a 2D TSNE projection of all the objects in the dataset, represented by their attribute vetctor.

SUN Attributes Dataset

at Hays Lab | 2011

To download this dataset, please visit the project webpage. The image on the left is a montage of 4 imgs from the dataset that include the attribute 'camping'.

Teaching:

Practical Machine Learning Tool (Leadership in Environmental and Digital innovation for Sustainability (LEADS) Summer School 2022)

GFolder with course materials

Introduction to CNNs and basic ML tools for Climate Change applications Syllabus.

Learn ML to Impact Climate Change (Leadership in Environmental and Digital innovation for Sustainability (LEADS) Summer School 2021)

Course Website

Deep Learning for Computer Vision: Tufts University Spring 2017

Course Website

Introduction to Deep Learning for Computer Vision Syllabus.

CSCI 2951T: Data-driven Computer Vision, Brown University

Course Website

Seminar course covering topics in State of the Art Computer Vision Syllabus.

Workshops:

  • Fall 2017 : COCO + Places 2017: Workshop for the COCO and Places challenges at ICCV 2017.
  • Fall 2017 : GroupSight 2017: Second Workshop on Human Computation for Image and Video Analysis @ HCOMP 2017.
  • Fall 2016 : ILSVRC + COCO 2016: Workshop for the COCO and ImageNet challenges at ECCV 2016.
  • Fall 2016 : GroupSight 2016: Workshop on Human Computation for Image and Video Analysis @ HCOMP 2016.
  • Fall 2015 : ILSVRC + COCO 2015: Workshop for the COCO and ImageNet challenges at ICCV 2015.
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