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  3. Adapting Dense Vision-language Relationships For Multi-label Classification With Partial Label.
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  3. Adapting Dense Vision-language Relationships For Multi-label Classification With Partial Label.

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

Adapting Dense Vision-Language Relationships for Multi-label Classification with Partial Label.

Cheng Chen, Yifan Zhao, Jia Li

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |June 24, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    This study introduces a Language-driven Dense Semantic Adaptor (LDSA) to improve multi-label image classification with incomplete data. LDSA effectively learns from partial annotations by leveraging multimodal models, achieving state-of-the-art results.

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    Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
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    Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

    Published on: February 8, 2019

    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

    Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
    07:31

    Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

    Published on: February 8, 2019

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Multi-label image classification with incomplete annotations is challenging.
    • Existing methods often rely on strong assumptions, leading to overfitting.
    • There is a need for more robust methods to handle missing semantic information.

    Purpose of the Study:

    • To propose a novel method, the Language-driven Dense Semantic Adaptor (LDSA), for multi-label image classification with incomplete annotations.
    • To leverage multimodal pretrained models like CLIP for improved semantic understanding.
    • To overcome the limitations of existing methods that suffer from unstable semantic mistakes and overfitting.

    Main Methods:

    • Developed a Language-driven Dense Semantic Adaptor (LDSA) utilizing multimodal pretrained CLIP models.
  • Introduced a densely contrastive adaptor to create dense visual contrastive constraints.
  • Proposed a language-driven interactive decoder with class-specific prompt tuning for adapting language proxies to visual domains.

    • Main Results:

    • Achieved a new state-of-the-art performance on public multi-label classification benchmarks.
    • Demonstrated the effectiveness of LDSA in learning from incomplete annotations.
    • Experimental results confirmed the method's ability to handle missing semantic information.

    Conclusions:

    • The proposed LDSA method significantly advances multi-label image classification with incomplete annotations.
    • LDSA effectively excavates prior-adaptive relationships from multimodal models.
    • Interpretable analyses show LDSA discovers implicit semantic relationships through prior-adaptive learning.