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Related Concept Videos

Purposive Learning01:22

Purposive Learning

207
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
207
Cognitive Learning01:21

Cognitive Learning

526
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
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Observational Learning01:12

Observational Learning

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
314
Visual Agnosia01:12

Visual Agnosia

304
Visual agnosia is a condition characterized by the inability to recognize visually presented objects despite having normal vision. For instance, a person with visual agnosia can describe the shape and color of an object but cannot identify or name it. This impairment does not affect their visual field, acuity, color vision, brightness discrimination, language, or memory. An example of this condition in a social setting is someone at a dinner party asking for "that silver thing with a round...
304
Associative Learning01:27

Associative Learning

579
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
579
Implicit Memories01:24

Implicit Memories

194
Implicit memories, also known as non-declarative memories, are long-term memories that function outside of conscious awareness. These memories influence behavior and skills without explicit knowledge. This type of memory is evident in tasks like playing tennis, snowboarding, and texting. Implicit memory has three subsystems: procedural memory, conditioning, and priming. This type of memory is essential in various activities, from everyday tasks to specialized skills.
One key aspect of implicit...
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Related Experiment Video

Updated: Sep 13, 2025

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects
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E-InMeMo: Enhanced Prompting for Visual In-Context Learning.

Jiahao Zhang1, Bowen Wang1, Hong Liu2

  • 1D3 Center, The University of Osaka, Osaka 565-0871, Japan.

Journal of Imaging
|July 25, 2025
PubMed
Summary
This summary is machine-generated.

Enhanced Instruct Memore (E-InMeMo) optimizes visual in-context learning by adding learnable prompts to image pairs. This method significantly boosts performance in tasks like foreground segmentation and object detection.

Keywords:
image segmentationmedical image analysisprompt enhancementvisual in-context learning

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Large-scale models excel in generalizability across tasks.
  • In-context learning (ICL) uses prompts without parameter updates, adapted from NLP to computer vision.
  • Visual ICL relies heavily on the quality of input-output image pairs (in-context pairs).

Purpose of the Study:

  • To introduce a novel approach, Enhanced Instruct Memore (E-InMeMo), for optimizing visual ICL prompts.
  • To enhance the effectiveness of in-context learning in computer vision tasks.

Main Methods:

  • Proposing E-InMeMo, which integrates learnable perturbations into in-context pairs.
  • Optimizing prompting strategies for visual ICL through these perturbations.

Main Results:

  • E-InMeMo demonstrated superior performance compared to state-of-the-art methods on standard vision tasks.
  • Achieved significant improvements: 7.99% in mIoU for foreground segmentation and 17.04% for single object detection.
  • Outperformed baseline methods lacking learnable prompts.

Conclusions:

  • E-InMeMo is a lightweight and effective strategy for enhancing visual ICL.
  • Learnable perturbations offer a promising direction for optimizing ICL prompts in computer vision.