Reinforced active learning for image segmentation

• URL: http://arxiv.org/abs/2002.06583v1
• Date: Sun, 16 Feb 2020 14:03:06 GMT
• Title: Reinforced active learning for image segmentation
• Authors: Arantxa Casanova, Pedro O. Pinheiro, Negar Rostamzadeh, Christopher J. Pal
• Abstract summary: We present a new active learning strategy for semantic segmentation based on deep reinforcement learning (RL) An agent learns a policy to select a subset of small informative image regions -- opposed to entire images -- to be labeled from a pool of unlabeled data. Our method proposes a new modification of the deep Q-network (DQN) formulation for active learning, adapting it to the large-scale nature of semantic segmentation problems.
• Score: 34.096237671643145
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning-based approaches for semantic segmentation have two inherent challenges. First, acquiring pixel-wise labels is expensive and time-consuming. Second, realistic segmentation datasets are highly unbalanced: some categories are much more abundant than others, biasing the performance to the most represented ones. In this paper, we are interested in focusing human labelling effort on a small subset of a larger pool of data, minimizing this effort while maximizing performance of a segmentation model on a hold-out set. We present a new active learning strategy for semantic segmentation based on deep reinforcement learning (RL). An agent learns a policy to select a subset of small informative image regions -- opposed to entire images -- to be labeled, from a pool of unlabeled data. The region selection decision is made based on predictions and uncertainties of the segmentation model being trained. Our method proposes a new modification of the deep Q-network (DQN) formulation for active learning, adapting it to the large-scale nature of semantic segmentation problems. We test the proof of concept in CamVid and provide results in the large-scale dataset Cityscapes. On Cityscapes, our deep RL region-based DQN approach requires roughly 30% less additional labeled data than our most competitive baseline to reach the same performance. Moreover, we find that our method asks for more labels of under-represented categories compared to the baselines, improving their performance and helping to mitigate class imbalance.

Related papers

• Two-Step Active Learning for Instance Segmentation with Uncertainty and Diversity Sampling [20.982992381790034]
  We propose a post-hoc active learning algorithm that integrates uncertainty-based sampling with diversity-based sampling. Our proposed algorithm is not only simple and easy to implement, but it also delivers superior performance on various datasets.
  arXiv  Detail & Related papers  (2023-09-28T03:40:30Z)
• Few-shot Image Classification based on Gradual Machine Learning [6.935034849731568]
  Few-shot image classification aims to accurately classify unlabeled images using only a few labeled samples. We propose a novel approach based on the non-i.i.d paradigm of gradual machine learning (GML) We show that the proposed approach can improve the SOTA performance by 1-5% in terms of accuracy.
  arXiv  Detail & Related papers  (2023-07-28T12:30:41Z)
• Exploring Open-Vocabulary Semantic Segmentation without Human Labels [76.15862573035565]
  We present ZeroSeg, a novel method that leverages the existing pretrained vision-language model (VL) to train semantic segmentation models. ZeroSeg overcomes this by distilling the visual concepts learned by VL models into a set of segment tokens, each summarizing a localized region of the target image. Our approach achieves state-of-the-art performance when compared to other zero-shot segmentation methods under the same training data.
  arXiv  Detail & Related papers  (2023-06-01T08:47:06Z)
• Semantic Segmentation with Active Semi-Supervised Learning [23.79742108127707]
  We propose a novel algorithm that combines active learning and semi-supervised learning. Our method obtains over 95% of the network's performance on the full-training set.
  arXiv  Detail & Related papers  (2022-03-21T04:16:25Z)
• Learning What Not to Segment: A New Perspective on Few-Shot Segmentation [63.910211095033596]
  Recently few-shot segmentation (FSS) has been extensively developed. This paper proposes a fresh and straightforward insight to alleviate the problem. In light of the unique nature of the proposed approach, we also extend it to a more realistic but challenging setting.
  arXiv  Detail & Related papers  (2022-03-15T03:08:27Z)
• Dominant Set-based Active Learning for Text Classification and its Application to Online Social Media [0.0]
  We present a novel pool-based active learning method for the training of large unlabeled corpus with minimum annotation cost. Our proposed method does not have any parameters to be tuned, making it dataset-independent. Our method achieves a higher performance in comparison to the state-of-the-art active learning strategies.
  arXiv  Detail & Related papers  (2022-01-28T19:19:03Z)
• CvS: Classification via Segmentation For Small Datasets [52.821178654631254]
  This paper presents CvS, a cost-effective classifier for small datasets that derives the classification labels from predicting the segmentation maps. We evaluate the effectiveness of our framework on diverse problems showing that CvS is able to achieve much higher classification results compared to previous methods when given only a handful of examples.
  arXiv  Detail & Related papers  (2021-10-29T18:41:15Z)
• Large-scale Unsupervised Semantic Segmentation [163.3568726730319]
  We propose a new problem of large-scale unsupervised semantic segmentation (LUSS) with a newly created benchmark dataset to track the research progress. Based on the ImageNet dataset, we propose the ImageNet-S dataset with 1.2 million training images and 40k high-quality semantic segmentation annotations for evaluation.
  arXiv  Detail & Related papers  (2021-06-06T15:02:11Z)
• Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation [49.90178055521207]
  This work addresses weakly supervised semantic segmentation (WSSS), with the goal of bridging the gap between image-level annotations and pixel-level segmentation. We formulate WSSS as a novel group-wise learning task that explicitly models semantic dependencies in a group of images to estimate more reliable pseudo ground-truths. In particular, we devise a graph neural network (GNN) for group-wise semantic mining, wherein input images are represented as graph nodes.
  arXiv  Detail & Related papers  (2020-12-09T12:40:13Z)
• The Devil is in Classification: A Simple Framework for Long-tail Object Detection and Instance Segmentation [93.17367076148348]
  We investigate performance drop of the state-of-the-art two-stage instance segmentation model Mask R-CNN on the recent long-tail LVIS dataset. We unveil that a major cause is the inaccurate classification of object proposals. We propose a simple calibration framework to more effectively alleviate classification head bias with a bi-level class balanced sampling approach.
  arXiv  Detail & Related papers  (2020-07-23T12:49:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.