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Nowadays, autonomous driving technology has become widely prevalent. The intelligent vehicles have been equipped with various sensors (e.g. vision sensors, LiDAR, depth cameras etc.). Among them, the vision systems with tailored semantic segmentation and perception algorithms play critical roles in scene understanding. However, the traditional supe...
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... are made in Tables 1 and 2 between our method and other state-of-the-art supervised few-shot segmentation approaches and self-supervised semantic segmentation approaches. Here, avg represents the mean intersection over union, 5 i represents the average category segmentation accuracy of all categories in the i-th fold, and FSS-1000 represents the segmentation accuracy on the FSS-1000 dataset. ...Context 2
... supervised models utilized the ground-truth segmentation mask during usual fold-based training, whereas the unsupervised models were trained on a training set without the ground truth. As shown in Table 1, we achieved the best results among all the self-supervised methods and even surpassed two of the fully supervised methods. Similarly for COCO and FSS-1000, we also achieved the best overall results among all the self-supervised methods, exceeding two of the fully supervised methods (on COCO). ...Context 3
... attribute the superb results on the FSS-1000 dataset to the unsupervised saliency regions being more prominent and free of noise and to the relatively high within-class image similarity. It is worth noting that MaskSplit surpasses our method on 5 3 in both Tables 1 and 2. The reason is that MaskSplit masks out all the background regions of the supported image (i.e., masked pooling) during the self-supervised training process. ...Similar publications
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