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Related Experiment Video

Updated: May 11, 2026

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies
07:34

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies

Published on: November 7, 2025

Enhancing training collections for image annotation: an instance-weighted mixture modeling approach.

Neela Sawant1, James Z Wang, Jia Li

  • 1College of Information Sciences and Technology, The Pennsylvania State University, University Park, PA 16802, USA. nks125@psu.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|May 16, 2013
PubMed
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This study introduces ARTEMIS, an enhanced automatic training image selection method. ARTEMIS improves visual concept learning by adapting to noisy data, matching manually curated dataset performance.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Automatic training data selection for visual concept learning is hindered by inaccurate tags and atypical images.
  • Manually curated datasets remain preferred for image annotation systems despite their labor-intensive nature.

Purpose of the Study:

  • To introduce ARTEMIS, a novel scheme for enhancing automatic training image selection.
  • To improve the robustness and efficiency of visual concept learning systems.

Main Methods:

  • ARTEMIS utilizes an instance-weighted mixture modeling framework to adapt to noisy training examples.
  • An optimization algorithm learns instance-weights and mixture parameters, handling diverse data modalities via hypothetical local mapping.
  • Training examples are selected using a likelihood-based image ranking approach.

Related Experiment Videos

Last Updated: May 11, 2026

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies
07:34

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies

Published on: November 7, 2025

Main Results:

  • ARTEMIS demonstrates superior resilience to noise compared to baseline methods in large-scale data collection.
  • The image annotation system trained with ARTEMIS achieves performance comparable to systems using manually curated datasets.

Conclusions:

  • ARTEMIS offers a robust and effective alternative to manual data curation for training image selection.
  • The proposed method significantly enhances the reliability of automatic visual concept learning systems.