- Vision AI weekly
- Posts
- Vision AI weekly: Issue 03
Vision AI weekly: Issue 03
Another exciting week in the Vision AI ecosystem!
sriram Venkatesan
August 13, 2025 • Estimated Reading Time: 4 minutes
🌟 Editor's Note
Welcome to another exciting week in the Vision AI ecosystem! We've got a packed newsletter full of insights, events, and inspiring stories from the heart of innovation.
🗓️ Event Highlights
Computer Vision at SIGGRAPH 2025:
A new integration between NVIDIA Omniverse NuRec and Voxel51's FiftyOne addresses data quality issues for autonomous vehicles (AV) and advanced driver assistance systems (ADAS). This data pipeline allows developers to validate and correct datasets before they are used for neural reconstruction and simulation workflows. The collaboration ensures high-integrity datasets are used from the start, which is essential for meeting the stringent validation and regulatory standards required for safety-critical applications. [link]
Deadlines:
3DV’26 : 5 days
WACV’26 (R2, reg) : 31 days
WACV’26 (R2, paper) : 38 days
ICLR’26 (abs) : 38 days
ICLR’26 (paper) : 43 days
🚀 Case study
Computer vision is transforming mining by enhancing safety and productivity. It automates visual monitoring, such as verifying personal protective equipment, reducing injuries, boosting efficiency, and aiding ore sorting and waste reduction. Convolve AI, led by Dhruv Sharma, is actively partnering with mining companies to pilot such systems, emphasizing how “AI is replacing human eyes.” Despite challenges like harsh environments, infrastructure constraints, and legacy system integration, the long-term benefits of real-time monitoring and operational optimization make the technology compelling. [link]
🦄 Startup Spotlight
IQGeo has acquired Deepomatic, integrating its AI computer vision platform into geospatial network software for telecom and utility operators. This enables real-time field data capture, automated quality control, and continuously updated digital twins. Already used by 30,000+ field workers at major operators, Deepomatic analyzes ~1M operations monthly, boosting accuracy and efficiency. The move advances autonomous network capabilities, laying the foundation for predictive, agent-driven infrastructure management.
🔥 Paper to Factory
Object detection (OD) is critical for computer vision applications but challenging to run on resource-constrained IoT devices powered by energy-efficient microcontrollers. The surge of IoT, expected to exceed 150 billion devices by 2030, amplifies the need for efficient solutions. TinyML enables OD on ultra-low-power devices, supporting real-time edge processing. Unlike prior surveys, this paper focuses on optimization techniques—quantization, pruning, knowledge distillation, and neural architecture search—for TinyML-based OD. It covers theoretical approaches and practical implementations, compares KPIs like accuracy and efficiency for OD on microcontrollers, and introduces a public repository to track ongoing advancements.
🏆 Community Spotlight:
In the recent AI Engineer summit, Peter Robicheaux form Roboflow highlights how computer vision is evolving beyond benchmarks
In their recent blog post , Lightly revealed that LightlyTrain now supports DINOv2 model
Reddit / X corner:
Ultralytics discuss on how to export YOLO11 models to Apple Core ML
A latest Reddit post discusses interactive visualizations of PyTorch computer vision models within notebooks
Another post discusses on how to hack a dataset importer to get LeRobot format data into FiftyOne
Till next time,