Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
[HTML][HTML] Large-scale road extraction from high-resolution remote sensing images based on a weakly-supervised structural and orientational consistency constraint …
Fully supervised road segmentation neural networks from remote sensing images rely on a
large number of densely labeled road samples, limiting their potential in large-scale …
large number of densely labeled road samples, limiting their potential in large-scale …
Test time adaptation with regularized loss for weakly supervised salient object detection
It is well known that CNNs tend to overfit to the training data. Test-time adaptation is an
extreme approach to deal with overfitting: given a test image, the aim is to adapt the trained …
extreme approach to deal with overfitting: given a test image, the aim is to adapt the trained …
Single stage weakly supervised semantic segmentation of complex scenes
The costly process of obtaining semantic segmentation labels has driven research towards
weakly supervised semantic segmentation (WSSS) methods, using only image-level, point …
weakly supervised semantic segmentation (WSSS) methods, using only image-level, point …
Advancing spatial map** for satellite image road segmentation with multi-head attention
Remote sensing imaging is an interesting field, particularly in road areas. Road
segmentation has become crucial in several areas, such as transportation network …
segmentation has become crucial in several areas, such as transportation network …
Unsupervised Losses for Clustering and Segmentation of Images: Theories & Optimization Algorithms
Z Zhang - 2024 - uwspace.uwaterloo.ca
Unsupervised losses are common for tasks with limited human annotations. In clustering,
they are used to group data without any labels. In semi-supervised or weakly-supervised …
they are used to group data without any labels. In semi-supervised or weakly-supervised …
Higher-order Losses and Optimization for Low-level and Deep Segmentation
D Marin - 2021 - uwspace.uwaterloo.ca
Regularized objectives are common in low-level and deep segmentation. Regularization
incorporates prior knowledge into objectives or losses. It represents constraints necessary to …
incorporates prior knowledge into objectives or losses. It represents constraints necessary to …