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Related Concept Videos

Downsampling01:20

Downsampling

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When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
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Related Experiment Video

Updated: Jul 6, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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PASS: Patch Automatic Skip Scheme for Efficient On-Device Video Perception.

Qihua Zhou, Song Guo, Jun Pan

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |January 8, 2024
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    Summary
    This summary is machine-generated.

    Patch Automatic Skip Scheme (PASS) accelerates video perception on edge devices by intelligently skipping redundant computations at the patch level. This task-independent method improves efficiency without sacrificing accuracy.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Real-time video perception on resource-constrained edge devices faces challenges with accuracy and hardware overhead.
    • Current methods often require task-specific hardware or pre-optimized models, limiting generalizability.

    Purpose of the Study:

    • To develop a general, task-independent methodology for accelerating video perception tasks on edge devices.
    • To introduce the Patch Automatic Skip Scheme (PASS) for efficient computation by selectively skipping redundant patches.

    Main Methods:

    • PASS utilizes learnable gates within convolution layers to identify and skip non-critical image patches.
    • A novel self-supervisory procedure optimizes these gates by learning contrastive representations from frame sequences.
    • Pre-trained gates act as plug-and-play modules for various neural backbones.

    Main Results:

    • PASS enables significant acceleration of diverse video-based downstream tasks.
    • Achieved up to 9.43x speedup in 3D pose estimation (MobileHumanPose) and 12.19x in multiple object tracking (FairMOT) on NVIDIA Jetson Nano.
    • Demonstrated effective acceleration without compromising model accuracy.

    Conclusions:

    • PASS offers a general and effective solution for accelerating video perception on edge devices.
    • The task-independent nature and plug-and-play modules make PASS adaptable to various applications.
    • This approach addresses the critical need for efficient computation in real-time edge AI.