Webedge-aware loss function is proposed to refine the boundaries directly from the semantic segmentation prediction for more robust and discriminative features. The effectiveness of the proposed model was demonstrated using two popular outdoor datasets—Cityscapes and CamVid—and the NYUDv2 indoor benchmark dataset. WebMar 1, 2024 · Fig. 1 illustrates the overall structure of the proposed Edge-Aware U-Net. The network consists of three parts, semantic segmentation flow, edge-gated flow, and edge downsampling flow. The latter two edge-aware flows are introduce to emphasize the sensitivity of edges and lead to more accurate segmentation results of finer retinal vessels.

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WebApr 11, 2024 · edge-aware loss function leads to better results than training. with MSE. Apart from that, Li et al. [15] use a resolution. decrement parameter (layer with stride 2) at the first CNN. WebEdge-Aware Losses: To validate the effectiveness of the loss supervision, we have trained our network without enforcing the supervision of the edge loss during the learning process, but with the same architecture. Table1 shows that our network performs very similarly to V-Net [7] without edge supervision, since bayha berkheim

Efficient Transformer for Remote Sensing Image Segmentation

WebDU-DEAL: Depth upsampling based on deep edge-aware learning (Pattern Recognition), Wang, Zhihui, et al. WAJCSR: Weakly Aligned Joint Cross-Modality Super Resolution (In Proceedings of the 2024 International Conference on Robotics Systems and Vehicle Technology), Shacht, Guy, et al. WebI. Shape-aware Loss Shape-aware loss [14] as the name suggests takes shape into account. Generally, all loss functions work at pixel level, how-ever, Shape-aware loss calculates the average point to curve Euclidean distance among points around curve of predicted segmentation to the ground truth and use it as coefficient to cross-entropy … WebDec 1, 2024 · An edge-aware loss function (EA loss), based on the dice loss, is presented to guide the EaNet to refine both the pixel-level and context-level information directly … david godoy suarez