ViewAL: Active Learning With Viewpoint Entropy for Semantic Segmentation

By Yawar Siddiqui, Julien P. C. Valentin, Matthias Nießner. 2019

190 citations recorded by the source corpus.

We propose ViewAL, a novel active learning strategy for semantic segmentation that exploits viewpoint consistency in multi-view datasets. Our core idea is that inconsistencies in model predictions across viewpoints provide a very reliable measure of uncertainty and encourage the model to perform well irrespective of the viewpoint under which objects are observed. To incorporate this uncertainty measure, we introduce a new viewpoint entropy formulation, which is the basis of our active learning strategy. In addition, we propose uncertainty computations on a superpixel level, which exploits inherently localized signal in the segmentation task, directly lowering the annotation costs. This combination of viewpoint entropy and the use of superpixels allows to efficiently select samples that are highly informative for improving the network. We demonstrate that our proposed active learning strategy not only yields the best-performing models for the same amount of required labeled data, but also significantly reduces labeling effort. For instance, our method achieves 95% of maximum achievable network performance using only 7%, 17%, and 24% labeled data on SceneNet-RGBD, ScanNet, and Matterport3D, respectively. On these datasets, the best state-of-the-art method achieves the same performance with 14%, 27% and 33% labeled data. Finally, we demonstrate that labeling using superpixels yields the same quality of ground-truth compared to labeling whole images, but requires 25% less time.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Few-Shot Continual Active Learning by a Robot (2022, methodology): “Unlike most active learning setups [18, 14], in FoCAL the model does not have access to a training set in each increment for training the model to predict uncertain object classes.”
Online Learning of Objects through Curiosity-Driven Active Learning (2021, background): “In recent years, attempts have been made to develop active learning techniques to reduce the cost of labelling [7], [8], [9], [10], [11].”
Learning Novel Objects Continually Through Curiosity (2021, background): “In recent years, attempts have been made to develop active learning techniques to reduce the cost of labelling [7], [8], [9], [10], [11].”
Semantic Curiosity for Active Visual Learning (2020, unknown): “esearchers have also used multi-view consistency to learn about 3D shape from weak supervision [49]. We instead leverage spatio-temporal consistency as a cue to identify what the model does not know. [46] is more directly related, but we tackle the problem in an embodied context and study how to nav”
Textual Data Augmentation for Efficient Active Learning on Tiny Datasets (2020, background): “Hence, this approach has not been very successful with deep learning models (Siddiqui et al., 2019).”