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Titlebook: Advances in Visual Computing; 14th International S George Bebis,Richard Boyle,Panpan Xu Conference proceedings 2019 Springer Nature Switzer

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發(fā)表于 2025-3-23 09:42:30 | 只看該作者
Adaptive Attention Model for Lidar Instance Segmentation safely and swiftly maneuver through complex urban streets without the intervention of human drivers. In contrast to recent detection-based approaches?[., .], we formulate the problem as a point-wise segmentation problem and focus on improving the recognition of small objects, which is very challeng
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發(fā)表于 2025-3-23 15:50:10 | 只看該作者
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發(fā)表于 2025-3-23 18:01:42 | 只看該作者
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發(fā)表于 2025-3-23 22:32:34 | 只看該作者
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發(fā)表于 2025-3-24 02:20:36 | 只看該作者
Resolution-Independent Meshes of Superpixelspixel was traditionally considered as a small cluster of square-based pixels that have similar color intensities and are closely located to each other. In this discrete model the boundaries of superpixels often have irregular zigzags consisting of horizontal or vertical edges from a given pixel grid
16#
發(fā)表于 2025-3-24 10:24:51 | 只看該作者
0302-9743 I; ST: Vision for Remote Sensing and Infrastructure Inspection; Computer Graphics II; Applications II; Deep Learning II; Virtual Reality II; Object Recognition/Detection/Categorization; and Poster. .978-3-030-33719-3978-3-030-33720-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
17#
發(fā)表于 2025-3-24 10:50:16 | 只看該作者
Thomas J. Snyder,Jayne Gackenbachlarge computational cost, we have developed an algorithm based on biorthogonal system to conduct the computation efficiently. In the experiments, we show that REAP can conduct pruning with smaller sacrifice of the model performances than several existing state-of-the-art methods such as CP [.], ThiNet [.], DCP [.], and so on.
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發(fā)表于 2025-3-24 18:40:07 | 只看該作者
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發(fā)表于 2025-3-24 19:55:24 | 只看該作者
20#
發(fā)表于 2025-3-25 02:39:32 | 只看該作者
Reconstruction Error Aware Pruning for Accelerating Neural Networkslarge computational cost, we have developed an algorithm based on biorthogonal system to conduct the computation efficiently. In the experiments, we show that REAP can conduct pruning with smaller sacrifice of the model performances than several existing state-of-the-art methods such as CP [.], ThiNet [.], DCP [.], and so on.
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