Search Results for author: Jonathan Krause

Found 15 papers, 3 papers with code

Domain-specific optimization and diverse evaluation of self-supervised models for histopathology

no code implementations20 Oct 2023 Jeremy Lai, Faruk Ahmed, Supriya Vijay, Tiam Jaroensri, Jessica Loo, Saurabh Vyawahare, Saloni Agarwal, Fayaz Jamil, Yossi Matias, Greg S. Corrado, Dale R. Webster, Jonathan Krause, Yun Liu, Po-Hsuan Cameron Chen, Ellery Wulczyn, David F. Steiner

Foundation models in histopathology that learn general representations across a wide range of tissue types, diagnoses, and magnifications offer the potential to reduce the data, compute, and technical expertise necessary to develop task-specific deep learning models with the required level of model performance.

Self-Supervised Learning

Improving Medical Annotation Quality to Decrease Labeling Burden Using Stratified Noisy Cross-Validation

no code implementations22 Sep 2020 Joy Hsu, Sonia Phene, Akinori Mitani, Jieying Luo, Naama Hammel, Jonathan Krause, Rory Sayres

For instance, Noisy Cross-Validation splits the training data into halves, and has been shown to identify low-quality labels in computer vision tasks; but it has not been applied to medical imaging tasks specifically.

Tool Detection and Operative Skill Assessment in Surgical Videos Using Region-Based Convolutional Neural Networks

no code implementations24 Feb 2018 Amy Jin, Serena Yeung, Jeffrey Jopling, Jonathan Krause, Dan Azagury, Arnold Milstein, Li Fei-Fei

We show that our method both effectively detects the spatial bounds of tools as well as significantly outperforms existing methods on tool presence detection.

Fine-Grained Car Detection for Visual Census Estimation

no code implementations7 Sep 2017 Timnit Gebru, Jonathan Krause, Yi-Lun Wang, Duyun Chen, Jia Deng, Li Fei-Fei

In this work, we leverage the ubiquity of Google Street View images and develop a computer vision pipeline to predict income, per capita carbon emission, crime rates and other city attributes from a single source of publicly available visual data.

Scalable Annotation of Fine-Grained Categories Without Experts

no code implementations7 Sep 2017 Timnit Gebru, Jonathan Krause, Jia Deng, Li Fei-Fei

We present a crowdsourcing workflow to collect image annotations for visually similar synthetic categories without requiring experts.

Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US

no code implementations22 Feb 2017 Timnit Gebru, Jonathan Krause, Yi-Lun Wang, Duyun Chen, Jia Deng, Erez Lieberman Aiden, Li Fei-Fei

The United States spends more than $1B each year on initiatives such as the American Community Survey (ACS), a labor-intensive door-to-door study that measures statistics relating to race, gender, education, occupation, unemployment, and other demographic factors.

A Hierarchical Approach for Generating Descriptive Image Paragraphs

3 code implementations CVPR 2017 Jonathan Krause, Justin Johnson, Ranjay Krishna, Li Fei-Fei

Recent progress on image captioning has made it possible to generate novel sentences describing images in natural language, but compressing an image into a single sentence can describe visual content in only coarse detail.

Dense Captioning Descriptive +2

The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition

1 code implementation20 Nov 2015 Jonathan Krause, Benjamin Sapp, Andrew Howard, Howard Zhou, Alexander Toshev, Tom Duerig, James Philbin, Li Fei-Fei

Current approaches for fine-grained recognition do the following: First, recruit experts to annotate a dataset of images, optionally also collecting more structured data in the form of part annotations and bounding boxes.

Ranked #4 on Fine-Grained Image Classification on CUB-200-2011 (using extra training data)

Active Learning Fine-Grained Image Classification

Fine-Grained Recognition Without Part Annotations

no code implementations CVPR 2015 Jonathan Krause, Hailin Jin, Jianchao Yang, Li Fei-Fei

Scaling up fine-grained recognition to all domains of fine-grained objects is a challenge the computer vision community will need to face in order to realize its goal of recognizing all object categories.

ImageNet Large Scale Visual Recognition Challenge

12 code implementations1 Sep 2014 Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, Li Fei-Fei

The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images.

General Classification Image Classification +3

Fine-Grained Crowdsourcing for Fine-Grained Recognition

no code implementations CVPR 2013 Jia Deng, Jonathan Krause, Li Fei-Fei

In this work, we include humans in the loop to help computers select discriminative features.

feature selection

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