Trending Misterio
iVoox
Descargar app Subir
iVoox Podcast & radio
Descargar app gratis
This Week in Machine Learning & AI Podcast
Zero-Shot Auto-Labeling: The End of Annotation for Computer Vision with Jason Corso - #735

Zero-Shot Auto-Labeling: The End of Annotation for Computer Vision with Jason Corso - #735 1q1q1o

10/6/2025 · 57:01
0
6
This Week in Machine Learning & AI Podcast

Descripción de Zero-Shot Auto-Labeling: The End of Annotation for Computer Vision with Jason Corso - #735 61v3d

Today, we're ed by Jason Corso, co-founder of Voxel51 and professor at the University of Michigan, to explore automated labeling in computer vision. Jason introduces FiftyOne, an open-source platform for visualizing datasets, analyzing models, and improving data quality. We focus on Voxel51’s recent research report, “Zero-shot auto-labeling rivals human performance,” which demonstrates how zero-shot auto-labeling with foundation models can yield to significant cost and time savings compared to traditional human annotation. Jason explains how auto-labels, despite being "noisier" at lower confidence thresholds, can lead to better downstream model performance. We also cover Voxel51's "verified auto-labeling" approach, which utilizes a "stoplight" QA workflow (green, yellow, red light) to minimize human review. Finally, we discuss the challenges of handling decision boundary uncertainty and out-of-domain classes, the differences between synthetic data generation in vision and language domains, and the potential of agentic labeling. The complete show notes for this episode can be found at https://twimlai.com/go/735. 6h5x5e

Comentarios de Zero-Shot Auto-Labeling: The End of Annotation for Computer Vision with Jason Corso - #735 2v3v3e

Este programa no acepta comentarios anónimos. ¡Regístrate para comentar!
Te recomendamos
Ir a Internet y tecnología