Learning Semantic Segmentation with Query Points Supervision on Aerial Images

• URL: http://arxiv.org/abs/2309.05490v2
• Date: Mon, 5 Aug 2024 18:57:42 GMT
• Title: Learning Semantic Segmentation with Query Points Supervision on Aerial Images
• Authors: Santiago Rivier, Carlos Hinojosa, Silvio Giancola, Bernard Ghanem,
• Abstract summary: We present a weakly supervised learning algorithm to train semantic segmentation algorithms. Our proposed approach performs accurate semantic segmentation and improves efficiency by significantly reducing the cost and time required for manual annotation.
• Score: 57.09251327650334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic segmentation is crucial in remote sensing, where high-resolution satellite images are segmented into meaningful regions. Recent advancements in deep learning have significantly improved satellite image segmentation. However, most of these methods are typically trained in fully supervised settings that require high-quality pixel-level annotations, which are expensive and time-consuming to obtain. In this work, we present a weakly supervised learning algorithm to train semantic segmentation algorithms that only rely on query point annotations instead of full mask labels. Our proposed approach performs accurate semantic segmentation and improves efficiency by significantly reducing the cost and time required for manual annotation. Specifically, we generate superpixels and extend the query point labels into those superpixels that group similar meaningful semantics. Then, we train semantic segmentation models supervised with images partially labeled with the superpixel pseudo-labels. We benchmark our weakly supervised training approach on an aerial image dataset and different semantic segmentation architectures, showing that we can reach competitive performance compared to fully supervised training while reducing the annotation effort. The code of our proposed approach is publicly available at: https://github.com/santiago2205/LSSQPS.

Related papers

• Location-Aware Self-Supervised Transformers [74.76585889813207]
  We propose to pretrain networks for semantic segmentation by predicting the relative location of image parts. We control the difficulty of the task by masking a subset of the reference patch features visible to those of the query. Our experiments show that this location-aware pretraining leads to representations that transfer competitively to several challenging semantic segmentation benchmarks.
  arXiv  Detail & Related papers  (2022-12-05T16:24:29Z)
• A Pixel-Level Meta-Learner for Weakly Supervised Few-Shot Semantic Segmentation [40.27705176115985]
  Few-shot semantic segmentation addresses the learning task in which only few images with ground truth pixel-level labels are available for the novel classes of interest. We propose a novel meta-learning framework, which predicts pseudo pixel-level segmentation masks from a limited amount of data and their semantic labels. Our proposed learning model can be viewed as a pixel-level meta-learner.
  arXiv  Detail & Related papers  (2021-11-02T08:28:11Z)
• Maximize the Exploration of Congeneric Semantics for Weakly Supervised Semantic Segmentation [27.155133686127474]
  We construct a graph neural network (P-GNN) based on the self-detected patches from different images that contain the same class labels. We conduct experiments on the popular PASCAL VOC 2012 benchmarks, and our model yields state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-10-08T08:59:16Z)
• Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learning [28.498782661888775]
  We formulate weakly supervised segmentation as a semi-supervised metric learning problem. We propose 4 types of contrastive relationships between pixels and segments in the feature space. We deliver a universal weakly supervised segmenter with significant gains on Pascal VOC and DensePose.
  arXiv  Detail & Related papers  (2021-05-03T15:49:01Z)
• A Closer Look at Self-training for Zero-Label Semantic Segmentation [53.4488444382874]
  Being able to segment unseen classes not observed during training is an important technical challenge in deep learning. Prior zero-label semantic segmentation works approach this task by learning visual-semantic embeddings or generative models. We propose a consistency regularizer to filter out noisy pseudo-labels by taking the intersections of the pseudo-labels generated from different augmentations of the same image.
  arXiv  Detail & Related papers  (2021-04-21T14:34:33Z)
• Exploring Cross-Image Pixel Contrast for Semantic Segmentation [130.22216825377618]
  We propose a pixel-wise contrastive framework for semantic segmentation in the fully supervised setting. The core idea is to enforce pixel embeddings belonging to a same semantic class to be more similar than embeddings from different classes. Our method can be effortlessly incorporated into existing segmentation frameworks without extra overhead during testing.
  arXiv  Detail & Related papers  (2021-01-28T11:35:32Z)
• 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)
• PCAMs: Weakly Supervised Semantic Segmentation Using Point Supervision [12.284208932393073]
  This paper presents a novel procedure for producing semantic segmentation from images given some point level annotations. We propose training a CNN that is normally fully supervised using our pseudo labels in place of ground truth labels. Our method achieves state of the art results for point supervised semantic segmentation on the PASCAL VOC 2012 dataset citeeveringham2010pascal, even outperforming state of the art methods for stronger bounding box and squiggle supervision.
  arXiv  Detail & Related papers  (2020-07-10T21:25:27Z)
• Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentation [128.03739769844736]
  Two neural co-attentions are incorporated into the classifier to capture cross-image semantic similarities and differences. In addition to boosting object pattern learning, the co-attention can leverage context from other related images to improve localization map inference. Our algorithm sets new state-of-the-arts on all these settings, demonstrating well its efficacy and generalizability.
  arXiv  Detail & Related papers  (2020-07-03T21:53:46Z)
• Discovering Latent Classes for Semi-Supervised Semantic Segmentation [18.5909667833129]
  This paper studies the problem of semi-supervised semantic segmentation. We learn latent classes consistent with semantic classes on labeled images. We show that the proposed method achieves state of the art results for semi-supervised semantic segmentation.
  arXiv  Detail & Related papers  (2019-12-30T14:16:24Z)

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.