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如题,如果LSM-HAWP wireframe detection model 只在ShanghaiTech数据集训练,不在其他数据集上训练,会不会影响在其他数据集上的实验效果?谢谢!
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我们只在ShanghaiTech上训练,在其他数据集上表现也比较良好。因为主要也是针对建筑方面的情况,自然场景直线比较少。另外可以借鉴SOLD2这种自监督方式来到其他数据集上finetune。 https://openaccess.thecvf.com/content/CVPR2021/papers/Pautrat_SOLD2_Self-Supervised_Occlusion-Aware_Line_Description_and_Detection_CVPR_2021_paper.pdf
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谢谢!有个困惑,为啥论文里的在 comprehensive Places2 (P2C) (随机挑选的10个类别带自然场景和man-made场景的数据)比在man-made Places2 (P2M)(10个类别只包含man-made场景的数据)上效果好(Table 1),man-made Places2 (P2M)数据更贴近建筑方面,效果应该更好才对
这可能是因为P2M的数据结构较多,相比自然场景的P2C本身难度就更高的原因
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如题,如果LSM-HAWP wireframe detection model 只在ShanghaiTech数据集训练,不在其他数据集上训练,会不会影响在其他数据集上的实验效果?谢谢!
The text was updated successfully, but these errors were encountered: