Titlebook: Computer Vision – ECCV 2022 Workshops; Tel Aviv, Israel, Oc Leonid Karlinsky,Tomer Michaeli,Ko Nishino Conference proceedings 2023 The Edit

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https://doi.org/10.1007/978-3-8349-9632-9 demand. Many of the methods emphasize optimization of a specific per-layer . (DoF), such as grid step size, preconditioning factors, nudges to weights and biases, often chained to others in multi-step solutions. Here we rethink quantized network parameterization in HW-aware fashion, towards a unifi

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ABOUT

Juvenile

cunning

https://doi.org/10.1007/978-3-8349-9632-9dget costs, inefficient productivity, and poor performance. This paper addresses the challenge of high-accuracy primitive instance segmentation from point clouds with the support of IFC model as a core stage to facilitate maintaining a geometric digital twin during the construction stage. Keeping th

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Research in Management Accounting & Controlor (3-Dimensional Particle Measurement; estimates the particle size distribution of material) utilizing RGB images and depth maps of mining material on the conveyor belt. Human annotations for material categories on sensor-generated data are scarce and cost-intensive. Currently, representation learn

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botany

Computer Vision – ECCV 2022 Workshops978-3-031-25082-8Series ISSN 0302-9743 Series E-ISSN 1611-3349

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https://doi.org/10.1007/978-3-031-22573-4ity of reconstructed images. We propose novel channel pruning and knowledge distillation techniques that are specialized for image inpainting models with mask information. Experimental results demonstrate that our compressed inpainting model with only one-tenth of the model size achieves similar performance to the full model.