Class-incremental semantic segmentation (CSS) requires that a model learn to segment new classes without forgetting how to segment previous ones: this is typically achieved by distilling the current knowledge an
Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic Segmentation Dipam Goswami†§ Rene´ Schuster† Joost van de Weijer‡ Didier Stricker† dipamgoswami01@gmail.com rene.schuster@dfki.de joost@...
Few-shot Class-Incremental Semantic Segmentation via Pseudo-Labeling and Knowledge Distillation Link to our Paper:https://arxiv.org/abs/2308.02790 Prerequisites and Requirements We tested our code under Ubuntu 20.04 and Windows with - Python 3.11.4 - Cuda 11.8 - PyTorch 2.0.1 ...
Class-Incremental Semantic Segmentation (CISS) aims to learn new classes without forgetting the old ones, using only the labels of the new classes. To achieve this, two popular strategies are employed: 1) pseudo-labeling and knowledge distillation to preserve prior knowledge; and 2) background ...
9 p. Transverse waves observed in a fibril with the MiHI prototype 44 p. Holography and Causality in the Karch-Randall Braneworld 13 p. ReVision: High-Quality, Low-Cost Video Generation with Explicit 3D Physics Modeling for Complex Motion and Interaction 13 p. SPARO: Surface-code Paul...
Antiadversarially Manipulated Attributions for Weakly and Semisupervised Semantic Segmentation. CVPR, 2021. 双层优化问题(Bilevel Optimization Problems, BOP) BOP旨在解决嵌套优化问题,其中外层优化依赖于内层优化的结果。BOP在超参数选择 [29] 和元学习 [13] 等领域表现出色。在CIL任务中,已有工作利用BOP交替优化...
(2021) proposed the first attempt to solve incremental few-shot semantic segmentation. They proposed PIFS, which combines prototype learning with knowledge distillation. In the base stage, PIFS trains the network on base data to develop the capability of feature extraction. In the FSL stage, PIFS...
[16] proposes feature-level knowledge distillation when applying continual learning [37] into semantic segmentation task [53, 52, 11]. Rehearsal Strategy: For replaying past experience, lots of CIL methods [49, 8, 42] allocate a memory to store ex- emplars of old...
论文地址: https://arxiv.org/abs/2004.00440 目录 一、贡献点 二、方法 2.1 triple loss 2.2 NCM(nearest class mean)分类器 2.3 Semantic Drift Compensation 三、实验及验证 3.1 SDC的作用 3.2 NCM及triple-loss 3.3 准确率 四、总结 一、贡献点 文章发表于CVPR2... 查看原文 [arxiv 20200628] Few-Shot...
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