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GAIA-Universe: Everything is Super-Netify.

Junran Peng, Qing Chang, Haoran Yin

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    GAIA-Universe (GAIA) offers customized computer vision models for specific needs, like latency and data types. This system efficiently generates tailored solutions for object detection and semantic segmentation tasks.

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    Area of Science:

    • Computer Vision
    • Machine Learning

    Background:

    • Large-scale pre-training is crucial in computer vision but expensive for specific applications.
    • Downstream tasks often have unique demands like latency constraints and specialized data distributions.

    Purpose of the Study:

    • To develop an automated and efficient system, GAIA-Universe (GAIA), for generating customized computer vision solutions.
    • To address the prohibitive cost of per-task pre-training for diverse application scenarios.

    Main Methods:

    • GAIA utilizes data union and super-net training to create tailored models.
    • The system searches for models that meet specific hardware, computation, and data domain requirements.

    Main Results:

    • GAIA provides powerful pre-trained weights and customized models for tasks with limited data.
    • Achieved strong results across multiple datasets including COCO, Objects365, and BDD100k.
    • On COCO, GAIA produced models with latencies from 16 ms to 53 ms and AP from 38.2 to 46.5.

    Conclusions:

    • GAIA-Universe offers a flexible and efficient approach to customized model generation for computer vision.
    • The system effectively caters to heterogeneous downstream needs, including hardware and data constraints.