Ashwin: Plug-and-Play System for Machine-Human Image Annotation

TL;DR

Ashwin is a flexible, modular system that integrates machine and human efforts for image annotation, allowing components to be easily added or replaced to improve annotation efficiency.

Contribution

It introduces a plug-and-play architecture for image annotation that combines machine learning and crowd input in a customizable framework.

Findings

Flexible integration of components enhances annotation accuracy.

Modular design allows easy adaptation to different annotation tasks.

System demonstrates improved efficiency over traditional methods.

Abstract

We present an end-to-end machine-human image annotation system where each component can be attached in a plug-and-play fashion. These components include Feature Extraction, Machine Classifier, Task Sampling and Crowd Consensus.