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    ECCV presenter says pretrained vision transformers enable fast real-world navigation

    The presenter describes an approach that uses one number per image patch and pretrains a navigation policy with lidar input before removing lidar.

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    TLDR

    The presenter of ECCV Poster #305 says a single numerical value per image patch from pretrained vision transformers enables fast real-world navigation. The approach, as described, includes a visual encoder distilled from different teacher models and a navigation policy pretrained with lidar input before lidar is removed.

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    2 Sources, first seen 19d ago

    Combined views

    2.2K

    2 Sources, first seen 19d ago

    30 likes
    19d ago
    first seen 19d ago
    30 likes
    3 comments
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    3 comments
    11 saves
    6 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

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    2 Sources

    @chriswolfvisionECCV Poster #305 3pm now: A scalar per patch from pre-trained ViTs enables fast moving navigation in the real world @JannySteeven, L. Antsfeld, myself (I will present) Visual enc distilled from heterog. teachers Attention proj Pretrain Policy w Lidar input => remove Lidar
    @CSProfKGDRT @chriswolfvision: ECCV Poster #305 3pm now: A scalar per patch from pre-trained ViTs enables fast moving navigation in the real world…

    2 Sources

    @chriswolfvisionECCV Poster #305 3pm now: A scalar per patch from pre-trained ViTs enables fast moving navigation in the real world @JannySteeven, L. Antsfeld, myself (I will present) Visual enc distilled from heterog. teachers Attention proj Pretrain Policy w Lidar input => remove Lidar
    @CSProfKGDRT @chriswolfvision: ECCV Poster #305 3pm now: A scalar per patch from pre-trained ViTs enables fast moving navigation in the real world…