First, our proposed PPT model introduces FocalNCE loss in patch-wise bidirectional contrastive learning to ensure high consistency between input and output images. Second, we propose a novel patch alignment loss to address the commonly observed misalignment issue in paired medical image datasets. Third...
A Global and Patch-wise Contrastive Loss for Accurate Automated Exudate Detection a patch-wise density loss, and a patch-wise edge-aware loss to improve the performance of these networks on the detection and segmentation of hard exuda... W Tang,Y Wang,K Cui,... - 《Arxiv》 被引量: 0...
首先,作者定义了CIC( (Increase of Confidence)的概念,既相对于baseline图片的置信度增量 对获取的特征图进行channel-wise遍历,对每层特征图进行上采样+归一化,与原始图片进行pixel-wise相乘融合,然后送进网络获取目标类别score(softmax后),减去baseline的目标类别score,获取CIC。再进行softmax操作来保证所有CIC之和为1...
We first propose a patch-based contrastive strategy that enforces locality conditions and richer feature representation. Secondly, we propose a patch stitching strategy to eliminate artifacts. We demonstrate, through our experiments, that our technique outperforms current state-of-the-art unsupervised ...
The experiment results suggest that (1) POPAR outperforms state-of-the-art (SoTA) self-supervised models with vision transformer backbone; (2) POPAR achieves significantly better performance over all three SoTA contrastive learning methods; and (3) POPAR also outperforms fully-supervised pretrained...
The two-staged contrastive learning based network (TCNet) is built. It is trained in two contrastive learning stages. Firstly, self-supervised contrastive learning is conducted, when the unlabeled PolSAR data is fully utilized for extracting the representation information; next, in the second contrasti...
P-MVSNet: Learning Patch-Wise matching confidence aggregation for Multi-View Stereo. In Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea, 27 October–2 November 2019; pp. 10452–10461. [Google Scholar] Yu, Z.; Gao, S. Fast-...