🚗📷 How can Vision-Language Models transform computer vision in industry?
At #ODSCWest, @DataScienceHarp (@Voxel51) leads a workshop on Visual AI for vehicle damage detection using the CarDD dataset + FiftyOne.
📅 Oct 28–30 | SF
🔗 hubs.li/Q03GX0jy0#VisualAI#VLMs
I saw FastVLM at CVPR Nashville and been patiently waiting for Transformers integration ever since.
It's here now.
• Every vision token adds LLM latency. ViT generates 576 tokens at 336×336. That's 576 forward passes through your decoder.
• FastVLM uses 144 tokens at 768×768…
Traditional annotation is slowing down the race to fully autonomous vehicles.
On Sept. 4, join @Porsche, Voxel51, and @databricks for an exclusive look at how cutting-edge VLMs, automated data pipelines, and human-in-the-loop feedback loops are transforming the way AV systems…
@nvidiaomniverse NuRec and @Voxel51 FiftyOne integration tackles a major bottleneck in AV development when working with multi-sensor datasets. As AV systems move from R&D to deployment, issues like misaligned sensor calibrations, drifting ego-poses, and timestamps often introduce…
In Part 1, @DataScienceHarp will walk through:
👉 Why standard vision models fail catastrophically on GUI tasks
👉 The annotation bottlenecks that make GUI datasets so expensive to create
👉 The platform fragmentation that makes "click a button" mean 20 different things across…
Here’s a peek at what’s on the agenda.
➡️ “Foundational models for generalist computer agents” – Raghav Kapoor, @Adobe
➡️ “Evaluating web agents with the BEARCUBS benchmark” – @yixiao_song, @UMassAmherst
➡️ “Building vision-based Android agents in production” – Rasul…
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