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Recapping the Computer Vision Meetup — August 10, 2023

We just wrapped up the August 10, 2023 Computer Vision Meetup, and if you missed it or want to revisit it, here’s a recap! In this blog post you’ll find the playback recordings, highlights from the presentations and Q&A, as well as the upcoming Meetup schedule so that you can join us at a future event.

First, Thanks for Voting for Your Favorite Charity!

In lieu of swag, we gave Meetup attendees the opportunity to help guide a $200 donation to charitable causes. The charity that received the highest number of votes this month was Coalition for Rainforest Nations, an organization on a mission to save the World’s last great rainforests to achieve environmental and social sustainability. We are sending this event’s charitable donation of $200 to Coalition for Rainforest Nations on behalf of the computer vision community!

Missed the Meetup? No problem. Here are playbacks and talk abstracts from the event.

Neural Congealing: Aligning Images to a Joint Semantic Atlas

Presenting Neural Congealing — a zero-shot self-supervised framework for detecting and jointly aligning semantically-common content across a given set of images. Our approach harnesses the power of pre-trained DINO-ViT features to learn: (i) a joint semantic atlas — a 2D grid that captures the mode of DINO-ViT features in the input set, and (ii) dense mappings from the unified atlas to each of the input images. We derive a new robust self-supervised framework that optimizes the atlas representation and mappings per image set, requiring only a few real-world images as input without any additional input information (e.g., segmentation masks). We design our losses and training paradigm to account only for the shared content under severe variations in appearance, pose, background clutter or other distracting objects, and demonstrate results on a plethora of challenging image sets including sets of mixed domains (e.g., aligning images depicting sculpture and artwork of cats), sets depicting related yet different object categories (e.g., dogs and tigers), or domains for which large-scale training data is scarce (e.g., coffee mugs).

Dolev Ofri-Amar is an MSc graduate from the Computer Science and Mathematics department at the Weizmann Institute of Science. Her interests are in computer vision and deep learning, mainly focused on image and video analysis and synthesis.

Resource links

Advancing Personalized Medicine and Radiotherapy through AI-Enabled Computer Vision

In this talk, we will explore the transformative role of computer vision and AI in the realm of medical imaging, focusing on its applications in personalized medicine and radiotherapy. We will delve into cutting-edge research, open source tools, and real-world use cases that demonstrate the potential of AI to enhance diagnostics, treatment planning, and outcome prediction. The presentation will showcase how computer vision techniques coupled with deep learning algorithms are enabling precise and personalized care for patients, revolutionizing the field of healthcare.

Speaker: Roushanak Rahmat, PhD is an accomplished AI scientist with a PhD in AI from Heriot-Watt University. She specializes in computer vision, medical imaging, and data science, developing advanced algorithms and models for healthcare applications. With expertise in deep learning, she has contributed significantly to AI projects in the healthcare sector, particularly in improving radiotherapy treatment. Roushanak is passionate about using AI to revolutionize healthcare and actively shares her knowledge as a public speaker, Women Techmaker ambassador, and through her Medium blog and YouTube channel.

A Practical Approach to Deep Learning for Computer Vision with Tensorflow 2

A detailed walkthrough of Neuralearn’s Deep Learning for Computer Vision course. We shall discuss at a high level how modern deep learning algorithms can be used in solving computer vision tasks using tools like Tensorflow 2, Hugging Face, Onnx, FastAPI, Weights and Biases and Albumentations, going from the basics of Machine Learning to deploying working computer vision solutions.

In this course, we lay much emphasis on practice, while explaining the theory behind the different algorithms we use. Learners from different backgrounds can easily follow along since efforts are made to explain every concept as clearly and concisely as possible. Because in recent times, deep learning is usually stereotyped as a math-heavy field, we explain in simple terms every math concept, so that learners who aren’t from a math-related background can start building real-world solutions easily.

Given that our focus is mainly on practice, we work on several projects in this course including a car price predictor, a malaria disease classifier, a human emotions detector, an object detector, a digit generator, an image segmenter, a people counter and an image generator.

Folefac Martins is an MSc graduate from the Electrical and Telecoms Engineering department at the National Advanced School of Engineering, Polytechnique Yaounde. His interests are in deep learning, helping people realize their potential and entrepreneurship.

Resource links

Join the Computer Vision Meetup!

Computer Vision Meetup membership has grown to more than 5,000 members in just one year! The goal of the Meetups is to bring together communities of data scientists, machine learning engineers, and open source enthusiasts who want to share and expand their knowledge of computer vision and complementary technologies. 

Join one of the 13 Meetup locations closest to your timezone.

We have exciting speakers already signed up over the next few months! Become a member of the Computer Vision Meetup closest to you, then register for the Zoom. 

What’s Next?

Up next on Aug 24 at 12 PM AEST we have a great line up speakers including:

  • Removing Backgrounds Automatically or with a User’s Language – Jizhizi Li, PhD, University of Sydney
  • Self-Supervised Representative Learning for Action Recognition in Videos – Vidhya Vinay, Co-Founder of
  • AI at the Edge: Optimizing Deep Learning Models for Real-World Applications – Raz Petel, SightX

Register for the Zoom here. You can find a complete schedule of upcoming Meetups on the Voxel51 Events page.

Get Involved!

There are a lot of ways to get involved in the Computer Vision Meetups. Reach out if you identify with any of these:

  • You’d like to speak at an upcoming Meetup
  • You have a physical meeting space in one of the Meetup locations and would like to make it available for a Meetup
  • You’d like to co-organize a Meetup
  • You’d like to co-sponsor a Meetup

Reach out to Meetup co-organizer Jimmy Guerrero on or ping me over LinkedIn to discuss how to get you plugged in.

The Computer Vision Meetup network is sponsored by Voxel51, the company behind the open source FiftyOne computer vision toolset. FiftyOne enables data science teams to improve the performance of their computer vision models by helping them curate high quality datasets, evaluate models, find mistakes, visualize embeddings, and get to production faster. It’s easy to get started, in just a few minutes.