Exploring Spatial Diversity for Region-based Active Learning

• URL: http://arxiv.org/abs/2507.17367v1
• Date: Wed, 23 Jul 2025 10:04:25 GMT
• Title: Exploring Spatial Diversity for Region-based Active Learning
• Authors: Lile Cai, Xun Xu, Lining Zhang, Chuan-Sheng Foo,
• Abstract summary: State-of-the-art methods for semantic segmentation are based on deep neural networks trained on large-scale labeled datasets.<n>We consider region-based active learning as a strategy to reduce annotation costs while maintaining high performance.
• Score: 20.866022165099626
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: State-of-the-art methods for semantic segmentation are based on deep neural networks trained on large-scale labeled datasets. Acquiring such datasets would incur large annotation costs, especially for dense pixel-level prediction tasks like semantic segmentation. We consider region-based active learning as a strategy to reduce annotation costs while maintaining high performance. In this setting, batches of informative image regions instead of entire images are selected for labeling. Importantly, we propose that enforcing local spatial diversity is beneficial for active learning in this case, and to incorporate spatial diversity along with the traditional active selection criterion, e.g., data sample uncertainty, in a unified optimization framework for region-based active learning. We apply this framework to the Cityscapes and PASCAL VOC datasets and demonstrate that the inclusion of spatial diversity effectively improves the performance of uncertainty-based and feature diversity-based active learning methods. Our framework achieves $95\%$ performance of fully supervised methods with only $5-9\%$ of the labeled pixels, outperforming all state-of-the-art region-based active learning methods for semantic segmentation.

Related papers

• Region-based Cluster Discrimination for Visual Representation Learning [30.79223671093668]
  Region-Aware Cluster Discrimination (RICE) is a novel method that enhances region-level visual and OCR capabilities.<n>RICE consistently outperforms previous methods on tasks, including segmentation, dense detection, and visual perception.
  arXiv  Detail & Related papers  (2025-07-26T17:47:09Z)
• Annotation-Efficient Polyp Segmentation via Active Learning [45.59503015577479]
  We propose a deep active learning framework for annotation-efficient polyp segmentation. In practice, we measure the uncertainty of each sample by examining the similarity between features masked by the prediction map of the polyp and the background area. We show that our proposed method achieved state-of-the-art performance compared to other competitors on both a public dataset and a large-scale in-house dataset.
  arXiv  Detail & Related papers  (2024-03-21T12:25:17Z)
• Progressive Feature Self-reinforcement for Weakly Supervised Semantic Segmentation [55.69128107473125]
  We propose a single-stage approach for Weakly Supervised Semantic (WSSS) with image-level labels. We adaptively partition the image content into deterministic regions (e.g., confident foreground and background) and uncertain regions (e.g., object boundaries and misclassified categories) for separate processing. Building upon this, we introduce a complementary self-enhancement method that constrains the semantic consistency between these confident regions and an augmented image with the same class labels.
  arXiv  Detail & Related papers  (2023-12-14T13:21:52Z)
• Hierarchical Point-based Active Learning for Semi-supervised Point Cloud Semantic Segmentation [48.40853126077237]
  It is labour-intensive to acquire large-scale point cloud data with point-wise labels. Active learning is one of the effective strategies to achieve this purpose but is still under-explored. This paper develops a hierarchical point-based active learning strategy.
  arXiv  Detail & Related papers  (2023-08-22T03:52:05Z)
• Point-Level Region Contrast for Object Detection Pre-Training [147.47349344401806]
  We present point-level region contrast, a self-supervised pre-training approach for the task of object detection. Our approach performs contrastive learning by directly sampling individual point pairs from different regions. Compared to an aggregated representation per region, our approach is more robust to the change in input region quality.
  arXiv  Detail & Related papers  (2022-02-09T18:56:41Z)
• Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentation [19.55572909866489]
  We propose a region-based active learning approach for semantic segmentation under a domain shift. Our algorithm, Active Learning via Region Impurity and Prediction Uncertainty (AL-RIPU), introduces a novel acquisition strategy characterizing the spatial adjacency of image regions. Our method only requires very few annotations to almost reach the supervised performance and substantially outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-11-25T06:40:58Z)
• Dense Contrastive Visual-Linguistic Pretraining [53.61233531733243]
  Several multimodal representation learning approaches have been proposed that jointly represent image and text. These approaches achieve superior performance by capturing high-level semantic information from large-scale multimodal pretraining. We propose unbiased Dense Contrastive Visual-Linguistic Pretraining to replace the region regression and classification with cross-modality region contrastive learning.
  arXiv  Detail & Related papers  (2021-09-24T07:20:13Z)
• Discriminative Region-based Multi-Label Zero-Shot Learning [145.0952336375342]
  Multi-label zero-shot learning (ZSL) is a more realistic counter-part of standard single-label ZSL. We propose an alternate approach towards region-based discriminability-preserving ZSL.
  arXiv  Detail & Related papers  (2021-08-20T17:56:47Z)
• ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation [28.478555264574865]
  ReDAL aims to automatically select only informative and diverse sub-scene regions for label acquisition. A diversity-aware selection algorithm is also developed to avoid redundant annotations. Experiments show that our method highly outperforms previous active learning strategies.
  arXiv  Detail & Related papers  (2021-07-25T09:40:48Z)
• Contextual Diversity for Active Learning [9.546771465714876]
  Large datasets restrict the use of deep convolutional neural networks (CNNs) for many practical applications. We introduce the notion of contextual diversity that captures the confusion associated with spatially co-occurring classes. Our studies show clear advantages of using contextual diversity for active learning.
  arXiv  Detail & Related papers  (2020-08-13T07:04:15Z)
• Attentive CutMix: An Enhanced Data Augmentation Approach for Deep Learning Based Image Classification [58.20132466198622]
  We propose Attentive CutMix, a naturally enhanced augmentation strategy based on CutMix. In each training iteration, we choose the most descriptive regions based on the intermediate attention maps from a feature extractor. Our proposed method is simple yet effective, easy to implement and can boost the baseline significantly.
  arXiv  Detail & Related papers  (2020-03-29T15:01:05Z)
• Reinforced active learning for image segmentation [34.096237671643145]
  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.
  arXiv  Detail & Related papers  (2020-02-16T14:03:06Z)

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.