The adapted representations often do not capture pixel-level domain shifts that are crucial for dense prediction tasks (e.g., semantic segmentation). In this paper, we present a novel pixel-wise adversarial domain adaptation algorithm. By leveraging image-to-image translation methods for data ...
MSDNet(Multi-Scale Dense Convolutional Networks,黄高)试图保持大分辨率的特征映射,这是文中架构最相似的工作。 但是,MSDNet的体系结构仍然使用不同分辨率的特征之间的卷积,这不能保留表示。 此外,它没有提供上采样途径来实现具有大分辨率和更多语义含义的特征。 MSDNet将多尺度机制引入其架构的目的是进行预算预测(do ...
PixPro(pixel-to-propagation) is an unsupervised visual feature learning approach by leveraging pixel-level pretext tasks. The learnt feature can be well transferred to downstream dense prediction tasks such as object detection and semantic segmentation.PixProachieves the best transferring performance on Pas...
Wang X, Zhang R, Shen C, Kong T, Li L (2021) Dense contrastive learning for self-supervised visual pre-training. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 3024–3033 Wang Z, Zhong Y, Miao Y, Ma L, Specia L (2022) Contrastive video-lan...
While HRNet-FCN can segment the cracks more completely, only ConvNeXt's prediction result showed the least false positives among the other model results. In the ninth section of Fig. 11, the results indicate that ConvNeXt demonstrates robust performance even under inconsistent illumination conditions...
在测试阶段,进行Dense Prediction,包括以下几个步骤:1、对所有层 的卷积响应进行前向计算。2、对映射到每个原始像素分辨率的响应进行双线性插值,产生超列特征的稠密像素网格。3、这些像素网格按像素输入MLP进行处理。 在训练阶段,由于计算量的变化,必须进行Parse Prediction。考虑给定一张图片X,和一个像素位置的稀疏集...
First, we introduce a self-attention module that learns dense pixel-level relations between features extracted by the backbone and neck, effectively preserving and exploring the spatial relationships of potential small objects. We then introduce an adaptive label assignment strategy that refines proposals...
It takes the conventional CNN architecture and replaces the fully connected layers with a convolutional layer by arguing that the dense layers can be thought of as doing 1 × 1 convolutions. The final convolution layer is then up-sampled using deconvolution to learn non-linear up-sampling and ...
Image distortion in the dense fishnet background in part A is less noticeable to the human eye compared to distortion on smooth areas and subjects in part B. This prompts us to separate the high-frequency components representing complex background information and to attack the neural network ...
Another target function is Dice [60] loss. Different than cross-entropy, Dice loss evaluates the overlap of two datasets that are measured in the range from 0 to 1. In image segmentation, the Dice score describes the overlap of sets, label, and prediction: ...