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Retrieval01:12

Retrieval

169
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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ER Retrieval Pathway01:45

ER Retrieval Pathway

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In the secretory pathway, vesicles transport proteins from one cellular compartment to another in forward transport to deliver the protein to its correct location. Occasionally, misfolded proteins and incorrect proteins escape their original compartments, and a retrieval pathway is used to return the escaped proteins to their original compartment.
The ER uses many checkpoints to prevent the entry of incorrectly folded or a resident protein as cargo onto a transport vesicle. These mechanisms...
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Sensory Modalities01:15

Sensory Modalities

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Sensation typically is the process by which the sensory receptors and sense organs detect stimuli from the internal and external environment and transmit this information to the central nervous system for processing.
General senses refer to the broad category of sensory information detected by receptors in the body and can be further grouped into somatic and visceral senses. Somatic sensations include touch, pressure, temperature, and pain and are essential for navigating our environment and...
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The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

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In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
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Elaborative Rehearsals01:07

Elaborative Rehearsals

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Elaborative rehearsal is a crucial cognitive strategy that strengthens information encoding in long-term memory by making meaningful connections between new data and pre-existing knowledge. This approach contrasts with maintenance rehearsal, which involves simple repetition without delving into the significance of the information. While maintenance rehearsal might temporarily keep information active in short-term memory, it is less effective for long-term retention.
The effectiveness of...
129
Chunking and Rehearsal in Sensory Memory01:22

Chunking and Rehearsal in Sensory Memory

293
Improving short-term memory can be achieved through techniques like chunking and rehearsal. Chunking involves organizing information into larger, more manageable units. This technique is particularly useful for information that exceeds the typical memory span of between five and nine items. For instance, logging into an online account with a password like "ta89vq0179gz" involves grouping letters and numbers into three chunks—ta89, vq01, and 79gz. It makes large amounts of...
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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InstructSee: Instruction-Aware and Feedback-Driven Multimodal Retrieval with Dynamic Query Generation.

Guihe Gu1,2,3, Yuan Xue1,2,3, Zhengqian Wu1,2,3

  • 1National Engineering Research Center for Multimedia Software (NERCMS), Wuhan 430072, China.

Sensors (Basel, Switzerland)
|August 28, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an instruction-aware framework for cross-modal retrieval, enhancing large language models (LLMs) to better understand user intent from complex instructions for improved visual-language alignment.

Keywords:
cross-modal retrievaldynamic query refinementlarge language models (LLMs)multimodal representation learningsemantic reasoning

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Natural Language Processing

Background:

  • Cross-modal retrieval aims to bridge different data types, notably aligning images with text.
  • Current methods face challenges in interpreting complex or evolving user instructions for accurate retrieval.
  • Understanding implicit user intent in multimodal queries remains a significant research gap.

Purpose of the Study:

  • To develop a novel cross-modal representation learning framework.
  • To enhance the ability of systems to capture user intent from natural language instructions.
  • To improve the adaptability and accuracy of multimodal retrieval systems.

Main Methods:

  • Proposed an instruction-aware dynamic query generation mechanism.
  • Integrated semantic reasoning capabilities of large language models (LLMs).
  • Dynamically constructed and iteratively refined query representations based on instructions and user feedback.

Main Results:

  • The framework significantly improved retrieval accuracy and adaptability on standard benchmarks.
  • Outperformed existing fixed-query baseline methods.
  • Demonstrated enhanced cross-modal alignment and generalization capabilities.

Conclusions:

  • The proposed framework effectively infers and adapts to implicit retrieval intent.
  • LLM-augmented dynamic query generation is crucial for complex instruction-based cross-modal retrieval.
  • The method offers a promising direction for more intuitive and accurate multimodal information access.