Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies
  1. Home
  3. Benefiting From Ood Samples In Open-set Semi-supervised Object Detection.
  1. Home
  3. Benefiting From Ood Samples In Open-set Semi-supervised Object Detection.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Isolated unilateral pulmonary vein atresia with hemoptysis in a child: A case report and literature review.

Medicine·2018
Same author

Clinical factors associated with intestinal strangulating obstruction and recurrence in adhesive small bowel obstruction: A retrospective study of 288 cases.

Medicine·2018
Same author

[Determination of organic acids in 1, 2-butylene oxide products by valve switch-ion chromatography].

Se pu = Chinese journal of chromatography·2018
Same author

Disruption of Planar Cell Polarity Pathway Attributable to Valproic Acid-Induced Congenital Heart Disease through Hdac3 Participation in Mice.

Chinese medical journal·2018
Same author

Multispectral and large bandwidth achromatic imaging with a single diffractive photon sieve.

Optics express·2018
Same author

Immunization with Chlamydia psittaci plasmid-encoded protein CPSIT_p7 induces partial protective immunity against chlamydia lung infection in mice.

Immunologic research·2018
Same journal

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

IEEE transactions on neural networks and learning systems·2026
Same journal

Hierarchical Semantic Concept Modeling for Generalizable Myocardial Pathology Segmentation on Multisequence CMR Images.

IEEE transactions on neural networks and learning systems·2026
Same journal

Stability of Time-Varying Impulsive Systems With State-Dependent Delay and Its Application in Complex Networks.

IEEE transactions on neural networks and learning systems·2026
Same journal

Adaptive Learning Control of Uncertain Systems via Weight and Intrinsic Plasticity-Based Neural Networks.

IEEE transactions on neural networks and learning systems·2026
Same journal

TTP-SSFL: Test-Time Personalization Self-Supervised Federated Learning for Accelerating MR Image Reconstruction.

IEEE transactions on neural networks and learning systems·2026
Same journal

ARKG: Adversarially Residual Knowledge Generalization to Open-Set Domain Adaptation.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Benefiting From OOD Samples in Open-Set Semi-Supervised Object Detection.

Yiqi Zou, Kuo Wang, Jichang Li

    IEEE Transactions on Neural Networks and Learning Systems
    |June 15, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    This study introduces a new method for open-set semi-supervised object detection (OSSOD) that effectively uses out-of-distribution (OOD) samples to improve in-distribution (ID) detection. The approach enhances feature learning and detection performance in open-set scenarios.

    Related Experiment Videos

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Open-set semi-supervised object detection (OSSOD) addresses challenges where unlabeled data may contain both in-distribution (ID) and out-of-distribution (OOD) samples.
    • Existing OSSOD methods typically aim to filter out OOD samples, potentially losing valuable information for feature learning.

    Purpose of the Study:

    • To develop a novel approach for OSSOD that leverages OOD samples to enhance the detection of ID categories.
    • To improve feature learning and overall detection performance in open-set conditions by effectively utilizing all unlabeled data.

    Main Methods:

    • Instance-level consistency regularization (ICR) applied to all detected instances, including OOD samples.
    • OOD-aware contrastive learning (OCL) to cluster ID objects and separate OOD samples in feature space.
    • Prototype-based multimetric adaptive matching (MAM) for reliable ID/OOD sample identification and weighted consistency regularization.

    Main Results:

    • The proposed method effectively utilizes OOD samples to optimize feature learning without negative impacts on semi-supervised learning.
    • Significant improvements in detection capability for ID categories in open-set scenarios were achieved.
    • The approach demonstrated superior performance compared to current state-of-the-art OSSOD methods.

    Conclusions:

    • Leveraging OOD samples in unlabeled data can boost feature learning and detection performance in OSSOD.
    • The proposed OCL and MAM methods provide effective mechanisms for distinguishing and utilizing ID and OOD samples.
    • This work offers a promising direction for advancing OSSOD by embracing, rather than excluding, OOD data.