[1] Yoo, Donggeun and In So Kweon. “Learning Loss for Active Learning.”2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)(2019): 93-102. [2] Sheikh, Rasha et al. “Gradient and Log-based Active Learning for Semantic Segmentation of Crop and Weed for Agricultural...
Finally, two acquisition functions are devised to select the most valuable samples with semantic difficulty. Competitive results on semantic segmentation benchmarks demonstrate that DEAL achieves state-of-the-art active learning performance and improves the performance of the hard semantic areas in ...
Randlanet在S3DIS上以初始学习率0.01训练了30个历时,每个历时后下降16%(在Semantic3D上以初始学习率0.01训练了50个历时,每个历时后下降8%)。其他超参数与默认值一致[11]。在S3DIS上,我们从未标注的集合中选择10k(在Semantic3D上为3k)信息量最大、最具代表性的超级点,然后利用阈值θ=0.9的噪声感知迭代标注策略对其...
Deep Active Learning for Named Entity Recognition Revisiting Superpixels for Active Learning in Semantic Segmentation With Realistic Annotation Costs Task-Aware Variational Adversarial Active Learning Multiple Instance Active Learning for Object Detection VaB-AL: Incorporating Class Imbalance and Difficulty With ...
【Multi-Anchor Active Domain Adaptation for Semantic Segmentation】—https://arxiv.org/abs/2108.08012 将目标域的分布无条件地与源域对齐可能会扭曲目标域数据的特有的信息。为此,作者提出了一种新颖的基于多锚点的主动学习策略,以协助域自适应语义分割任务。通过创新地采用多个点而不是单个质心,可以更好地将源域...
Cost-aware Active Learning for Semantic Segmentation With the recent developments of deep learning, the performance of semantic segmentation has been greatly improved. Creating a large set of traning data req... UO Kousuke,H Ito,M Matsubara,... - 《Proceedings of the Annual Conference of Jsai》...
Awesome Active Learning: https://github.com/baifanxxx/awesome-active-learning Note:前 1、2、3 节都是一些主动学习基础内容,也有很多文章做过类似的整理和介绍,如果你已经很了解了,可以直接跳到 4 节以后阅读。 介绍 主动学习是一种通过主动选择最有价值的样本进行标注的机器学习或人工智能方法。其目的是使用...
【Multi-Anchor Active Domain Adaptation for Semantic Segmentation】—https://arxiv.org/abs/2108.08012 将目标域的分布无条件地与源域对齐可能会扭曲目标域数据的特有的信息。为此,作者提出了一种新颖的基于多锚点的主动学习策略,以协助域自适应语义分割任务。通过创新地采用多个点而不是单个质心,可以更好地将源域...
Learning Loss for Active Learning https://arxiv.org/abs/1905.03677?context=cs.CV VAAL Variational Adversarial Active Learning https://arxiv.org/abs/1904.00370 应用场景 由于主动学习解决的是如何从无标签数据中选择价值高的样本进行标注,所以在数据标签难以获得、标注成本大的场景和实际问题中被广泛应用。
Semantic segmentation enables robots to perceive and reason about their environments beyond geometry. Most of such systems build upon deep learning approaches. As autonomous robots are commonly deployed in initially unknown environments, pre-training on static datasets cannot always capture the variety of...