Blog

Oct 30, 2019 · newsletter

Fuzzy People

While preparing for a recent edition of the Federated Learning talk I often give at conferences, I encountered this tweet, which includes a demonstration of real-time multi-person segmentation on a smartphone.

Some useful terminology here:

  • Object detection typically refers to identifying and localising objects (such as people!) in an image, and surrounding them with a bounding box.
  • Object segmentation labels each pixel of the image with a class (for instance: “person,” “car,” “cat,” …).

Segmenting multiple people in an image is substantially harder than segmenting a single person, because identifing the parts of the image that belong to each person essentially requires modeling each person’s pose. Without understanding human poses, how would our function-approximating neural network know that an arm around a shoulder belongs to person one, rather than person two?

This technology working on a mobile device in real-time is impressive.

There is good reason to be concerned about the malicious or misguided use of facial recognition technology to invade personal privacy. However, there are many conceivable applications for computer vision in public spaces which do not require incidental privacy violation - monitoring road traffic, for example.

A potential use of the raft of face-swapping apps recently launched is protecting the identity of people in the background of video or photos, while maintaining the realism of the image.

This naturally raises the question: what is private?

If you know me well, you can probably recognise me with an obfuscated face, based on clothes and surroundings. The overt pixellation in the mobile demo appealed to me. Because vision so viscerally connects to our senses, it strikes me as a fascinating arena in which to test theoretical measures of privacy.

I managed to find a few older papers covering this idea - though given it’s import, it feels under-explored.

When in doubt, play.

Thanks to the ready availability of pre-trained models, TensorFlow.js, Observable notebooks, and some free time at a conference, I could reasonably straightforwardly construct a person pixellator, which you can try for yourself here: Fuzzy People.

It can take an input image, such as this:

Brooklyn Bridge with pedestrians (image courtesy of unsplash.com)

And output (an attempt at) a more private version:

Brooklyn Bridge with fuzzy pedestrians

Since the picture is really intended to be of the Brooklyn Bridge and Manhattan skyline, we’ve done the pedestrians a favour and hidden their identities.

Clearly, it doesn’t work perfectly.

Some experimentation reveals it to work best when the people are in the foreground, clearly separated, there are only a few of them, and the picture was taken in reasonable lighting and weather conditions.

Nonetheless, I’m impressed by how well it sometimes does work, given that it is running some quite sophisticated models entirely inside a web browser.

The pixellation technique used is simple: for each pixel that the model identifies as a person (the model here is a combination of a bounding box with COCO-SSD, and single person segmentation with BodyPix), replace it with a randomly selected pixel from within an adjustable region. At low noise, where the region the random pixel is selected from is small, people in the images are still human identifiable. As noise is increased, people progressively become less person-like and more static.

Finding a less intrusive - but still highly private - means of masking people from images is left as an exercise to the research community.

The app is extremely early stage work, but I think credit is due to the open source community for providing pre-trained models to enable the creation of something that would have seemed magic a decade ago in a day or so of hacking. Certainly, we are past the point where some elements of computer vision can be considered as commodity: simply import the “detect person” function.

A side-note on the pace of change in this space:

Alas, much of my work is wasted. About a week after I made the notebook work, a multi-person version of BodyPix was released! While I confess to feeling slightly stung by having sunk a few hours into pixel manipulation to combine a bounding box model with a single-person segmentation model that has ultimately proved unnecessary, I’m excited to try out the new model. It certainly brings home the feeling of rapid progress in computer vision technology.

Read more

Newer
Nov 21, 2019 · newsletter
Older
Oct 30, 2019 · newsletter

Latest posts

May 5, 2022 · post

Neutralizing Subjectivity Bias with HuggingFace Transformers

by Andrew Reed · Subjective language is all around us – product advertisements, social marketing campaigns, personal opinion blogs, political propaganda, and news media, just to name a few examples. From a young age, we are taught the power of rhetoric as a means to influence others with our ideas and enact change in the world. As a result, this has become society’s default tone for broadcasting ideas. And while the ultimate morality of our rhetoric depends on the underlying intent (benevolent vs.
...read more
Mar 22, 2022 · post

An Introduction to Text Style Transfer

by Andrew Reed · Today’s world of natural language processing (NLP) is driven by powerful transformer-based models that can automatically caption images, answer open-ended questions, engage in free dialog, and summarize long-form bodies of text – of course, with varying degrees of success. Success here is typically measured by the accuracy (Did the model produce a correct response?) and fluency (Is the output coherent in the native language?) of the generated text. While these two measures of success are of top priority, they neglect a fundamental aspect of language – style.
...read more
Jan 31, 2022 · post

Why and How Convolutions Work for Video Classification

by Daniel Valdez-Balderas · Video classification is perhaps the simplest and most fundamental of the tasks in the field of video understanding. In this blog post, we’ll take a deep dive into why and how convolutions work for video classification. Our goal is to help the reader develop an intuition about the relationship between space (the image part of video) and time (the sequence part of video), and pave the way to a deep understanding of video classification algorithms.
...read more
Dec 14, 2021 · post

An Introduction to Video Understanding: Capabilities and Applications

by Daniel Valdez Balderas · Video footage constitutes a significant portion of all data in the world. The 30 thousand hours of video uploaded to Youtube every hour is a part of that data; another portion is produced by 770 million surveillance cameras globally. In addition to being plentiful, video data has tremendous capacity to store useful information. Its vastness, richness, and applicability make the understanding of video a key activity within the field of computer vision.
...read more
Sep 22, 2021 · post

Automatic Summarization from TextRank to Transformers

by Melanie Beck · Automatic summarization is a task in which a machine distills a large amount of data into a subset (the summary) that retains the most relevant and important information from the whole. While traditionally applied to text, automatic summarization can include other formats such as images or audio. In this article we’ll cover the main approaches to automatic text summarization, talk about what makes for a good summary, and introduce Summarize. – a summarization prototype we built that showcases several automatic summarization techniques.
...read more
Sep 21, 2021 · post

Extractive Summarization with Sentence-BERT

by Victor Dibia · In extractive summarization, the task is to identify a subset of text (e.g., sentences) from a document that can then be assembled into a summary. Overall, we can treat extractive summarization as a recommendation problem. That is, given a query, recommend a set of sentences that are relevant. The query here is the document, relevance is a measure of whether a given sentence belongs in the document summary. How we go about obtaining this measure of relevance varies (a common dilemma for any recommendation system).
...read more

Popular posts

Oct 30, 2019 · newsletter
Exciting Applications of Graph Neural Networks
Nov 14, 2018 · post
Federated learning: distributed machine learning with data locality and privacy
Apr 10, 2018 · post
PyTorch for Recommenders 101
Oct 4, 2017 · post
First Look: Using Three.js for 2D Data Visualization
Aug 22, 2016 · whitepaper
Under the Hood of the Variational Autoencoder (in Prose and Code)
Feb 24, 2016 · post
"Hello world" in Keras (or, Scikit-learn versus Keras)

Reports

In-depth guides to specific machine learning capabilities

Prototypes

Machine learning prototypes and interactive notebooks
Library

NeuralQA

A usable library for question answering on large datasets.
https://neuralqa.fastforwardlabs.com
Notebook

Explain BERT for Question Answering Models

Tensorflow 2.0 notebook to explain and visualize a HuggingFace BERT for Question Answering model.
https://colab.research.google.com/drive/1tTiOgJ7xvy3sjfiFC9OozbjAX1ho8WN9?usp=sharing
Notebooks

NLP for Question Answering

Ongoing posts and code documenting the process of building a question answering model.
https://qa.fastforwardlabs.com
Notebook

Interpretability Revisited: SHAP and LIME

Explore how to use LIME and SHAP for interpretability.
https://colab.research.google.com/drive/1pjPzsw_uZew-Zcz646JTkRDhF2GkPk0N

Cloudera Fast Forward Labs

Making the recently possible useful.

Cloudera Fast Forward Labs is an applied machine learning research group. Our mission is to empower enterprise data science practitioners to apply emergent academic research to production machine learning use cases in practical and socially responsible ways, while also driving innovation through the Cloudera ecosystem. Our team brings thoughtful, creative, and diverse perspectives to deeply researched work. In this way, we strive to help organizations make the most of their ML investment as well as educate and inspire the broader machine learning and data science community.

Cloudera   Blog   Twitter