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Related Concept Videos

Upsampling01:22

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
Carrier-Mediated Transport01:06

Carrier-Mediated Transport

Carrier-mediated transport is a pivotal process in drug absorption, particularly for lipid-insoluble drugs, and encompasses facilitated diffusion and active transport. Facilitated diffusion allows drugs to move along their concentration gradient without energy expenditure, while active transport utilizes ATP to drive drug movement against this gradient.
Active transport involves two types of membrane-spanning transporters: uptake and efflux. Uptake transporters are expressed in the small...
Downsampling01:20

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
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Methods of Medium Optimization

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Related Experiment Video

Updated: Jul 15, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Semantic Composition via Optimal Transport for Composed Image Retrieval.

Yifan Wang, Wuliang Huang, Chun Yuan

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |July 13, 2026
    PubMed
    Summary

    Semantic Composition via Optimal Transport (SCOT) improves composed image retrieval by effectively handling complex visual and textual relationships. This method optimizes computational resources and enhances semantic learning for better accuracy.

    Related Experiment Videos

    Last Updated: Jul 15, 2026

    End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
    03:31

    End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

    Published on: December 15, 2023

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Composed image retrieval faces challenges in understanding complex semantic relationships between reference images and modification sentences.
    • Existing methods struggle with global matching (ignoring fine details) or fragment-level matching (prone to noise and high computation).

    Purpose of the Study:

    • To propose a novel method, Semantic Composition via Optimal Transport (SCOT), for effective composed image retrieval.
    • To address limitations of current approaches in handling multi-modal query features and semantic discrepancies.

    Main Methods:

    • Introduced Hybrid-modal Affinity Summarization (HAS) to optimize computational resources by compacting salient relationships.
    • Employed optimal transport weights for Retain Prototype from Alignments (RAS) and Update Semantics from Enhanced Embeddings (USEE) to mitigate local noise and guide learning.
    • Preserved global structural characteristics while focusing on fine-grained semantic alignment.

    Main Results:

    • SCOT demonstrated advanced experimental performance on widely-used datasets: FashionIQ, CIRR, and Fashion200K.
    • Ablative studies confirmed the effectiveness of optimal transport guidance in learning preserved visual prototypes and updated semantics.

    Conclusions:

    • SCOT offers a robust solution for composed image retrieval by integrating global and local feature learning effectively.
    • The proposed method enhances the understanding of semantic composition for more accurate image search results.