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Related Concept Videos

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
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Associative Learning01:27

Associative Learning

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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...
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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...
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Introduction to Learning01:18

Introduction to Learning

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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
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Cognitive Learning01:21

Cognitive Learning

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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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Purposive Learning01:22

Purposive Learning

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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...
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Related Experiment Videos

Deep Information-Balanced Multimodal Learning.

Yang Qin, Yanglin Feng, Yuan Sun

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |April 7, 2026
    PubMed
    Summary
    This summary is machine-generated.

    Multimodal learning faces challenges due to imbalanced data optimization. This study introduces Multimodal Information Balance (MIB) theory and an Information-Balanced Multimodal Learning (IBML) framework to ensure balanced information retention for improved perception.

    Related Experiment Videos

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Information Theory

    Background:

    • Multimodal learning integrates diverse data for enhanced real-world understanding.
    • Data discrepancies across modalities cause imbalanced optimization, hindering performance.

    Purpose of the Study:

    • To address imbalanced optimization in multimodal learning.
    • To propose a theoretical framework for balancing complementary information retention across modalities.

    Main Methods:

    • Developed Multimodal Information Balance (MIB) theory and criterion.
    • Introduced an Information-Balanced Multimodal Learning (IBML) framework.
    • Incorporated Balance Information Optimization (BIO) and Task Complexity Modulation (TCM) modules.

    Main Results:

    • The MIB criterion adaptively balances complementary information preservation.
    • IBML framework achieves comprehensive and balanced multimodal information mining.
    • Experiments on eight datasets demonstrate IBML's superiority in audio-visual, image-text, and 2D-3D recognition.

    Conclusions:

    • The proposed MIB theory provides an explainable perspective on multimodal learning imbalances.
    • IBML framework effectively enhances multimodal fusion and learning performance.
    • The approach offers a robust solution for optimizing multimodal data integration.