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Found 6 papers, 1 papers with code
Organizing Background to Explore Latent Classes for Incremental Few-shot Semantic Segmentation
During incrementally learning novel classes, the data distribution of old classes will be destroyed, leading to catastrophic forgetting.
The Binary Quantized Neural Network for Dense Prediction via Specially Designed Upsampling and Attention
Then we further optimize the attention method which plays an important role in segmentation but has huge computation complexity.
Lifelong Learning and Selective Forgetting via Contrastive Strategy
The joint task of the two is dubbed Learning with Selective Forgetting (LSF).
Edge-guided and Class-balanced Active Learning for Semantic Segmentation of Aerial Images
Due to the gap between aerial and natural images, the previous AL methods are not ideal, mainly caused by unreasonable labeling units and the neglect of class imbalance.
Continual Learning for Image Segmentation with Dynamic Query
Image segmentation based on continual learning exhibits a critical drop of performance, mainly due to catastrophic forgetting and background shift, as they are required to incorporate new classes continually.
End-to-end Remote Sensing Change Detection of Unregistered Bi-temporal Images for Natural Disasters
Deep networks have demonstrated significant success in detecting changes in bi-temporal remote sensing images and have found applications in various fields.
