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Related Experiment Video

Updated: May 11, 2026

An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles
09:27

An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles

Published on: August 25, 2020

Toward open set recognition.

Walter J Scheirer1, Anderson de Rezende Rocha, Archana Sapkota

  • 1Department of Molecular and Cellular Biology, Harvard University, 52 Oxford St. NWL 209, Cambridge, MA 02138, USA. wscheirer@fas.harvard.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|May 18, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel "1-vs-set machine" for open set recognition in computer vision. This method effectively handles unknown classes during testing, outperforming existing algorithms in object recognition and face verification tasks.

Related Experiment Videos

Last Updated: May 11, 2026

An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles
09:27

An Emerging Target Paradigm to Evoke Fast Visuomotor Responses on Human Upper Limb Muscles

Published on: August 25, 2020

Area of Science:

  • Computer Vision
  • Machine Learning
  • Pattern Recognition

Background:

  • Traditional machine learning recognition algorithms operate in a 'closed set' setting, assuming all testing classes are known during training.
  • Real-world computer vision applications often involve 'open set' recognition, where algorithms encounter unknown classes not seen during training.

Purpose of the Study:

  • To address the limitations of existing algorithms in open set recognition by formalizing the problem as a constrained minimization.
  • To introduce a novel machine learning approach, the '1-vs-set machine', designed for robust open set recognition.

Main Methods:

  • Formalized open set recognition as a constrained minimization problem requiring strong generalization.
  • Developed a novel '1-vs-set machine' utilizing marginal distances from 1-class or binary Support Vector Machines (SVMs) with a linear kernel.
  • Conducted large-scale cross-dataset experiments on Caltech 256 and ImageNet for object recognition, and Labeled Faces in the Wild for face verification.

Main Results:

  • The proposed '1-vs-set machine' demonstrates superior performance in open set recognition tasks compared to traditional 1-class and binary SVMs.
  • Experiments validated the effectiveness of the methodology across diverse computer vision applications, including object recognition and face verification.
  • The approach successfully handles scenarios where unknown classes are presented during the testing phase.

Conclusions:

  • The '1-vs-set machine' offers a significant advancement in tackling the challenges of open set recognition in computer vision.
  • The methodology provides a more realistic and effective solution for real-world vision applications where encountering novel classes is common.
  • This work highlights the importance of generalization capabilities in machine learning models for robust performance in dynamic environments.