CV Brief · Monday, 11 May 2026
CV Brief
Research & Papers
Edge Deep Learning for Vision and Medical Diagnostics: Survey
Comprehensive review of edge deep learning paradigm for real-time CV applications, with focus on medical diagnostics. Directly addresses deployment constraints practitioners face: latency, power, and local processing without cloud dependency.
Read more →LookWhen: Fast Video Recognition via Selective Token Computation
Introduces selector-extractor framework that learns when, where, and what to compute in video, reducing transformer computational cost by exploiting temporal-spatial redundancy. Directly applicable to production video pipelines needing speed improvements.
Read more →Knowledge Transfer Scaling Laws for 3D Medical Imaging
Establishes scaling laws for multimodal 3D medical imaging pretraining across CT, MRI, PET modalities. Provides actionable guidance on mixture strategies and transfer learning for practitioners building 3D medical vision systems.
Read more →Tools & Releases
MachinaCheck: Multi-Agent CNC Manufacturability on AMD MI300X
MachinaCheck demonstrates a multi-agent system for evaluating CNC manufacturability, built on AMD MI300X hardware. This shows practical deployment of vision-language models for industrial quality assessment and design validation.
Read more →Tutorials & Guides
Combining SAM 3 Segmentation and Metric Depth for Distance Estimation
Hands-on integration of META's SAM 3 and MapAnything models to solve the practical problem of distance judgement in vision systems. Demonstrates combining multiple foundation models for real-world CV tasks.
Read more →Getting Started in CV/ML
Building Metallurgy Vision Pipeline from Foundation Models
Weekend project demonstrating how to build a working CV pipeline using HuggingFace pretrained models and commodity GPUs. Shows practical approach to domain-specific vision tasks without massive compute budgets.
Read more →For class imbalance: don't just augment the minority class. First ask whether the imbalance reflects real-world distribution. If it does, your model should reflect it too.