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Related Concept Videos

Perception01:28

Perception

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Perception is a fundamental psychological process that enables individuals to organize, interpret, and consciously experience sensory information. This process is crucial for understanding and interacting with the world around us. It includes both bottom-up and top-down processing, each playing a distinct role in how we perceive our environment.
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Perceiving others accurately is fundamental to effective communication and relationship-building. Social perception, a key concept in social psychology, refers to the cognitive processes through which individuals gather and interpret information about others to understand their actions, intentions, and motivations. This process extends beyond spoken words and overt behaviors, incorporating subtle nonverbal cues and contextual factors.Nonverbal Cues and Their SignificanceNonverbal cues play a...
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The human ear is not equally sensitive to all frequencies in the audible range. It may perceive sound waves with the same pressure but different frequencies as having different loudness. Moreover, the perception of sound waves depends on the health of an individual's ears, which decays with age. The health of one's ears may also be affected by regular exposure to loud noises.
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The actor-observer effect, a cognitive bias closely linked to the fundamental attribution error, refers to the tendency for individuals to attribute their behavior to external, situational factors while explaining others’ behavior in terms of internal, dispositional traits. This asymmetry in attribution significantly influences social perception and judgment.Cognitive Mechanisms Behind the EffectTwo primary psychological mechanisms contribute to the actor-observer effect: differences in...
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Pragmatic Communication in Multi-Agent Collaborative Perception.

Yue Hu, Xianghe Pang, Xiaoqi Qin

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |April 9, 2026
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    Summary
    This summary is machine-generated.

    This study introduces PragComm, a system for efficient collaborative perception in multi-agent scenarios. PragComm significantly reduces communication volume by transmitting only essential information, outperforming previous methods.

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    Area of Science:

    • Computer Science
    • Robotics
    • Artificial Intelligence

    Background:

    • Collaborative perception enhances agent abilities through message exchange, but faces a trade-off between perception and communication costs.
    • Existing methods transmit high-dimensional feature maps, leading to substantial communication overhead.
    • Optimizing the perception-communication trade-off is crucial for efficient multi-agent systems.

    Purpose of the Study:

    • To develop a communication-efficient collaborative perception system that minimizes data transmission.
    • To address the limitations of high communication costs in current collaborative perception approaches.
    • To propose a pragmatic communication strategy focusing on task-critical information.

    Main Methods:

    • Formulated a mathematical optimization framework for the perception-communication trade-off.
    • Proposed PragComm, a system featuring pragmatic message selection, representation, and collaborator selection.
    • Implemented pragmatic message selection for spatially and temporally sparse feature vectors.
    • Utilized a task-adaptive dictionary for pragmatic message representation, enabling communication via integer indices.
    • Developed pragmatic collaborator selection to identify beneficial collaborators and prune unnecessary links.

    Main Results:

    • PragComm demonstrated superior performance in collaborative 3D object detection and tracking tasks.
    • Achieved over 32.7K× lower communication volume compared to previous methods on the OPV2V dataset.
    • The system adapts effectively to various communication conditions.
    • Evaluated on real-world (V2V4Real) and simulation (OPV2V, V2X-SIM2.0) datasets.

    Conclusions:

    • PragComm offers a significant advancement in communication efficiency for collaborative perception.
    • The pragmatic communication strategy effectively balances perception ability and communication costs.
    • The proposed system shows strong potential for real-world multi-agent applications requiring efficient data exchange.