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Found 6 papers, 5 papers with code
Endpoints Weight Fusion for Class Incremental Semantic Segmentation
In our method, the model containing old knowledge is fused with the model retaining new knowledge in a dynamic fusion manner, strengthening the memory of old classes in ever-changing distributions.
Class-Incremental Semantic Segmentation
Incremental Learning
+1
Representation Compensation Networks for Continual Semantic Segmentation
In this work, we study the continual semantic segmentation problem, where the deep neural networks are required to incorporate new classes continually without catastrophic forgetting.
Ranked #1 on
Domain 1-1
on Cityscapes
Class Incremental Learning
Continual Semantic Segmentation
+16
Personalized Image Semantic Segmentation
In this paper, we address the problem of personalized image segmentation.
LayerCAM: Exploring Hierarchical Class Activation Maps for Localization
To evaluate the quality of the class activation maps produced by LayerCAM, we apply them to weakly-supervised object localization and semantic segmentation.
Delving Deep into Label Smoothing
Experiments demonstrate that based on the same classification models, the proposed approach can effectively improve the classification performance on CIFAR-100, ImageNet, and fine-grained datasets.
Deep Hough Transform for Semantic Line Detection
In addition to the proposed method, we design an evaluation metric to assess the quality of line detection and construct a large scale dataset for the line detection task.
Ranked #2 on
Line Detection
on NKL
