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Retrieval

Retrieval

Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
Recall involves accessing information without cues, such as during an essay test, where individuals must retrieve facts and concepts from memory unaided. Another example is remembering the name of a colleague...

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Related Experiment Video

Updated: Jul 28, 2025

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

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BCAN: Bidirectional Correct Attention Network for Cross-Modal Retrieval.

Yang Liu, Hong Liu, Huaqiu Wang

IEEE Transactions on Neural Networks and Learning Systems

|May 31, 2023

Summary

This summary is machine-generated.

This study introduces a Bidirectional Correct Attention Network (BCAN) to improve cross-modal retrieval by addressing semantic misalignment in attention mechanisms for vision and language tasks.

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Area of Science:

Computer Science
Artificial Intelligence
Natural Language Processing

Background:

Cross-modal retrieval aims to link image regions with text words.
Existing attention-based methods struggle with semantic misalignment due to fragmented analysis.
Challenges include distinguishing similar semantic fragments and handling irrelevant regions/words.

Purpose of the Study:

To propose a novel Bidirectional Correct Attention Network (BCAN) for enhanced cross-modal retrieval.
To introduce a concept of subfragment relevance to entire image/sentence semantics.
To correct attention weights for improved accuracy in vision-language tasks.

Main Methods:

Developed a Bidirectional Correct Attention Network (BCAN).
Introduced a novel correct attention mechanism with local and global similarity modeling.
Designed Global Correct Unit (GCU) and Local Correct Unit (LCU) to refine attention weights.

Main Results:

BCAN effectively addresses semantic misalignment in cross-modal retrieval.
The method outperforms existing attention-based approaches on MS-COCO and Flickr30K datasets.
Achieved competitive performance against state-of-the-art methods.

Conclusions:

The proposed BCAN significantly improves cross-modal retrieval accuracy.
The novel attention correction mechanism enhances the understanding of subfragment relevance.
BCAN offers a robust solution for vision-language understanding tasks.