本文已经被NeurIPS 2019(2019 Conference and Workshop on Neural Information Processing Systems)接收,论文为弱监督图像语义分割方法提出了一种全新的损失函数——门控全连接条件随机场损失即Gated CRF Loss,通过与传统交叉熵损失函数结合,应用于重量级语义分割模型DeepLab-v3plus
整体架构源于 WebQA 的参考论文 Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question。 这篇论文有几个特点: 直接将问题用 LSTM 编码后得到“问题编码”,然后拼接到材料的每一个词向量中 人工提取了 2 个共现特征 将最后的预测转化为了一个序列标注任务,用 CRF 解决 而DGCNN ...
By feeding these properties into a CRF based framework and utilizing the semantic information of images to boost the discriminant capability of the neural networks, the proposed system achieved superior performance for the aesthetic quality assessment. In our future work, different loss functions for ...
[3] Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv preprint arXiv:1412.7062 (2014) [4] Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger,...
Additionally, DeepLabv1 utilized a fully-connected Conditional Random Field (CRF) to enhance the model's ability to capture structural information, thereby solving fine-segmentation problems. DeepLabv2 (Chen et al., 2017a), proposed in 2017, incorporated the Atrous Spatial Pyramid Pooling (ASPP) ...
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. In: IEEE Transactions Pattern Analysis and Machine Intelligence (TPAMI), IEEE (2018a) Chen, X., Li, W...
CRF is incorporated into FCN by [29, 17] to encourage spatial and appearance consistency in the labelling outputs Affinity CNNs [2, 20] embed additional pixel-wise similarity loss into FCN for dense prediction 相似性CNN? add one data driven pooling layer on top of DeconvNet to smooth the ...
The mutational studies based on apo-CaM- NaV1.6 IQ motif complex revealed that substituting a single interact- ing residue of CaM with Ala resulted in complete loss of interaction with NaV1.6 IQ motif. This could probably explain why the interac- tions of apo-CaM-NaV1.2 IQ motif and apo-...
We use binary cross-entropy (CE) loss between the prediction and the ground truth to train our network and can be written as: 我们使用预测和真实值之间的二元交叉熵(CE)损失来训练我们的网络,可以写成: where w and h are the dimensions of the image, p(x, y) corresponds to the pixel in th...
The fluorescence signal of the J-aggre- galalttehse(λperxo =te 4o8l0ip nomso,mλeems =un 5t9i5l tnhme e)xwtearsnmaloKn+itcoornedceunptroantioanddwitaisoninocrfevaasleindobmyythciena, dwdhitiicohninoifti2a.t5e dMKK+Ceflfsluoxluftrioomn. The normalized ...