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Encord Monthly Wrap: June Industry Newsletter

July 1, 2024
7 mins
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Hi there,

Welcome to the Computer Vision Monthly Wrap for June 2024!

Here’s what you should expect:

  • 🎁 Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach
  • 📽️ Top CVPR 2024 papers, including the poster sessions
  • ⚒️ Developer resources to use for your next vision AI application
  • 🤝 New model releases in the computer vision and multimodal AI world

Let’s go! 🚀

light-callout-cta Encord released TTI-Eval, an open-source library to evaluate the performance of fine-tuned CLIP, domain-specific ones like BioCLIP models, and other VLMs on your dataset! Check out the getting started blog post. 📐

📜 Top Picks for Computer Vision Papers This Month

Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach

Researchers at Meta AI released a paper introducing an automatic data curation method for self-supervised learning that can create large, diverse, and balanced datasets without manual effort. 

The approach in the paper uses hierarchical k-means clustering and balanced sampling to curate high-quality datasets from raw data.

The method addresses imbalanced data representation in web-based collections, ensuring a more uniform distribution of diverse concepts.

What’s impressive? 🤯

  • The approach enables training self-supervised models on automatically curated datasets, which alleviates the need for costly manual labeling and curation
  • Hierarchical k-means clustering obtains uniform data clusters representing different concepts
  • Balanced sampling from the clusters ensures the curated dataset has an even distribution of concepts
  • Experiments on images, satellite data, and text show features trained on the auto-curated datasets match or exceed the performance of features from manually curated data

Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach | Encord

How can you apply it? ⚒️

  • Curate your own high-quality datasets from large raw data sources for self-supervised pre-training
  • Scale up model training by avoiding the bottleneck of manual data curation
  • Improve model robustness and generalization by training on diverse and balanced datasets
  • Apply the technique to various domains like computer vision, earth observation (remote-sensing), and natural language processing

Frederik Hvilshøj, Lead ML Engineer at Encord, spoke to the paper's first author and distilled (yes, I don’t excuse the pun 😁) insights from the paper and conversations. Watch the video on LinkedIn.

light-callout-cta 📜 Read the publication.

Top Papers and Poster Sessions from CVPR 2024

CVPR 2024 was quite an experience for many researchers, stakeholders, and engineers working on computer vision and multimodal AI problems. At Encord, we even released a fun game to get you battling it out with AI to win amazing prizes! 😎.

This article reviews the top papers presented at CVPR 2024, including the research highlights. Frederik also reviewed some of the papers that were presented during the poster session:

Frederik Hvilshøj's review on LinkedIn - Top Papers and Poster Sessions from CVPR 2024

🧑‍💻 Developer Resources You’d Find Useful

📰 Computer Vision In the News

Till next month, have a super-sparkly time!

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Written by

Stephen Oladele

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