fully convolutional architectures as is the case of FCN [15]. To incorporate translation variance into FCN, we construct a set of position-sensitive score maps by using a bank of specialized convolutional layers
An FCN naturally operates on an input of any size, and produces an output of corresponding (possibly resampled) spatial dimensions. A real-valued loss function composed with an FCN defines a task. If the loss function is a sum over the spatial dimensions of the final layer, `(x; θ) =...
该论文拿到了best paper候选的论文,在之后的PASCAL VOC2012,凡是涉及到图像语义分割的模型,都沿用了FCN的结构,并且这篇论文跟VGG的结构也很相似,区别只在于VGG最后的全连接层在FCN上替换为卷积层,因此在我们了解完VGG之后,再来了解FCN是很有意义的.这篇文章我们将对论文进行翻译,同时也是精读,希望读完之后能够有所...
为了让R-FCN拥有带RPN的特征,我们采用了4步交替训练[18],在RPN和R-FCN中交替训练。 测试 正如图2所描述的,RPN和R-FCN共享的特征地图(feature maps)在一个单尺度为600的图像上被计算。然后RPN部分选择出了ROI,而R-FCN部分评估了catagogy-wise scores和regresses bounding boxes(针对每一个物体项 的得分)和regr...
翻译论文汇总:https://github.com/SnailTyan/deep-learning-papers-translation R-FCN: Object Detection via Region-based Fully Convolutional Networks Abstract We present region-based, fully convolutional networks for accurate and efficient object detection. In contrast to previous region-based detectors such ...
A real-valued loss function composed with an FCN defines a task. If the loss function is a sum over the spatial dimensions of the final layer, `(x; θ) = P ij ` 0 (xij ; θ), its gradient will be a sum over the gradients of each of its spatial components. Thus stochastic gradi...
A real-valued loss function composed with an FCN defines a task. If the loss function is a sum over the spatial dimensions of the final layer, `(x; θ) = P ij ` 0 (xij ; θ), its gradient will be a sum over the gradients of each of its spatial components. Thus stochastic gradi...
翻译论文汇总:https://github.com/SnailTyan/deep-learning-papers-translation R-FCN: Object Detection via Region-based Fully Convolutional Networks Abstract We present region-based, fully convolutional networks for accurate and efficient object detection. In contrast to previous region-based detectors such ...
“sky”). An FCN can naturally learn a joint representation that simultaneously predicts both types of labels. We learn a two-headed version of FCN-16s with semantic and geometric prediction layers and losses. The learned model performs as well on both tasks as two independently trained models,...