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    Chapter and Conference Paper

    EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications

    In the pursuit of achieving ever-increasing accuracy, large and complex neural networks are usually developed. Such models demand high computational resources and therefore cannot be deployed on edge devices. ...

    Muhammad Maaz, Abdelrahman Shaker in Computer Vision – ECCV 2022 Workshops (2023)

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    Chapter and Conference Paper

    DoodleFormer: Creative Sketch Drawing with Transformers

    Creative sketching or doodling is an expressive activity, where imaginative and previously unseen depictions of everyday visual objects are drawn. Creative sketch image generation is a challenging vision probl...

    Ankan Kumar Bhunia, Salman Khan, Hisham Cholakkal in Computer Vision – ECCV 2022 (2022)

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    Chapter and Conference Paper

    Video Instance Segmentation via Multi-Scale Spatio-Temporal Split Attention Transformer

    State-of-the-art transformer-based video instance segmentation (VIS) approaches typically utilize either single-scale spatio-temporal features or per-frame multi-scale features during the attention computation...

    Omkar Thawakar, Sanath Narayan, Jiale Cao, Hisham Cholakkal in Computer Vision – ECCV 2022 (2022)

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    Chapter and Conference Paper

    SipMask: Spatial Information Preservation for Fast Image and Video Instance Segmentation

    Single-stage instance segmentation approaches have recently gained popularity due to their speed and simplicity, but are still lagging behind in accuracy, compared to two-stage methods. We propose a fast singl...

    Jiale Cao, Rao Muhammad Anwer, Hisham Cholakkal in Computer Vision – ECCV 2020 (2020)