Vision AI weekly: Issue 05

Another exciting week in the Vision AI ecosystem!

Author

sriram Venkatesan
September 24, 2025 • Estimated Reading Time: 5 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. We apologize that we weren’t able to publish the newsletter for the last four weeks.

Tool Spotlight

ROSE (Remove Objects with Side Effects) is a video inpainting framework addressing not just object removal but also their side effects—shadows, reflections, light, translucency, and mirrors. Due to limited paired video data, ROSE uses a 3D rendering engine to generate a large-scale synthetic dataset with diverse scenes. Built on a diffusion transformer, it employs reference-based erasing and side-effect supervision via differential masks. A new ROSE-Bench benchmark validates its superior performance and real-world generalization. [link]

🚀 Case study

Ultralytics’ latest article explains how combining CAD (Computer-Aided Design) and computer vision is transforming modern manufacturing. CAD provides precise digital blueprints; CAM (Computer-Aided Manufacturing) converts them into instructions (toolpaths, G-code) for machines.

Computer vision adds real-time feedback: from scan-to-CAD reverse engineering of physical parts, to augmented reality-guided assembly and automated quality inspection. Applications include automotive, aerospace, and precision finishing, where vision systems detect defects, misalignments, missing features, and surface flaws.

Benefits include faster design workflows, higher accuracy, less waste, better productivity and training. Challenges include high data requirements, costs, and integrating with legacy systems. [link]

🦄 Startup Spotlight

Agave Networks: Transparency & Tech in Recycled Metals Trade

Agave Networks is building a global, digital marketplace for recycled metals—focused on bringing trust, transparency, and efficiency to a market that’s traditionally opaque. Their core tools are:

What they do

  • AI-validated listings — using computer vision and geolocation to authenticate and grade metal lots.

  • Live shipment monitoring — a camera setup at loading, livestreamed so both buyer & seller can see that what’s in the listing is what gets shipped.

  • Secure payments & escrow — milestone-based, to reduce risk for both parties.

Who it serves

  • Buyers get verified materials before committing, real-time tracking of shipments, and access to trustworthy global suppliers.

  • Sellers get exposure to a vetted buyer base, traceability of shipments to reduce disputes, secure payments, and streamlined workflows (logistics, documentation etc.).

🔥 Paper to Factory

OcuViT is a novel vision transformer (ViT)-based model designed for automated retinal disease detection, specifically diabetic retinopathy (DR) and age-related macular degeneration (AMD).

Unlike traditional manual analysis, OcuViT streamlines diagnosis using transfer learning with a pre-trained ViT-Base-Patch16-224 model, adapted through a preprocessing pipeline that standardizes retinal fundus images. Validated on APTOS and IChallenge-AMD datasets, it achieves superior accuracy in binary, five-class DR, and AMD grading tasks, outperforming existing CNN and ViT methods. OcuViT demonstrates enhanced precision, robustness, and computational efficiency, highlighting the potential of ViT-based transfer learning in advancing reliable, automated ophthalmic diagnostics for early disease detection.

🏆 Community Spotlight:

  • In the recent Voxel 51 podcast, Jason Corso highlights the rise of Visual AI applications in manufacturing

  • In her recent blog, Dr. Paula Ramos, PhD, Senior DevRel at Voxel51, elaborates on the impact of Vision AI in agriculture

  • In their recent article, viso.ai elaborate on why Visual General Intelligence would be a game changer for industries

Reddit / X corner:

  • Ultralytics discuss on how to detect rotated objects with Ultralytics YOLO OBB task and how to train YOLO11 on the LVIS dataset for long-tail object detection

  • In this recent Reddit post, a user has built a gaze estimation pipeline using entirely synthetic training data

  • In this recent Reddit post, a user speaks about his difficulties in handling inconsistent bounding boxes

Till next time,

Vision AI weekly