Magisterial 发表于 2025-3-23 09:42:30
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 challenganachronistic 发表于 2025-3-23 15:50:10
http://reply.papertrans.cn/16/1502/150104/150104_12.png冥想后 发表于 2025-3-23 18:01:42
http://reply.papertrans.cn/16/1502/150104/150104_13.pngExcitotoxin 发表于 2025-3-23 22:32:34
http://reply.papertrans.cn/16/1502/150104/150104_14.png狗窝 发表于 2025-3-24 02:20:36
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我怕被刺穿 发表于 2025-3-24 10:24:51
0302-9743I; 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反话 发表于 2025-3-24 10:50:16
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.严厉批评 发表于 2025-3-24 18:40:07
http://reply.papertrans.cn/16/1502/150104/150104_18.png柳树;枯黄 发表于 2025-3-24 19:55:24
http://reply.papertrans.cn/16/1502/150104/150104_19.png出价 发表于 2025-3-25 02:39:32
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.