Semantic Segmentation of Remote Sensing Images with Sparse Annotations

• URL: http://arxiv.org/abs/2101.03492v1
• Date: Sun, 10 Jan 2021 07:21:21 GMT
• Title: Semantic Segmentation of Remote Sensing Images with Sparse Annotations
• Abstract summary: Training Convolutional Neural Networks (CNNs) for very high resolution images requires a large quantity of high-quality pixel-level annotations. We propose a framework for semantic segmentation of aerial images based on incomplete annotations. We propose the FEature and Spatial relaTional regulArization (FESTA) method to complement the supervised task with an unsupervised learning signal.
• Score: 23.22039010678414
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training Convolutional Neural Networks (CNNs) for very high resolution images requires a large quantity of high-quality pixel-level annotations, which is extremely labor- and time-consuming to produce. Moreover, professional photo interpreters might have to be involved for guaranteeing the correctness of annotations. To alleviate such a burden, we propose a framework for semantic segmentation of aerial images based on incomplete annotations, where annotators are asked to label a few pixels with easy-to-draw scribbles. To exploit these sparse scribbled annotations, we propose the FEature and Spatial relaTional regulArization (FESTA) method to complement the supervised task with an unsupervised learning signal that accounts for neighbourhood structures both in spatial and feature terms.

Related papers

• RADA: Region-Aware Dual-encoder Auxiliary learning for Barely-supervised Medical Image Segmentation [33.17607546116405]
  Barely-supervised learning reduces annotation burden by using only a few labeled slices per volume.<n>We propose RADA, a novel Region-Aware Dual-encoder Auxiliary learning pipeline.<n>The framework combines image-level fine-grained visual features with text-level semantic guidance, providing region-aware semantic supervision.
  arXiv  Detail & Related papers  (2026-04-13T08:26:26Z)
• Annotation-Free Open-Vocabulary Segmentation for Remote-Sensing Images [51.74614065919118]
  This paper introduces SegEarth-OV, the first framework for annotation-free open-vocabulary segmentation of RS images.<n>We propose SimFeatUp, a universal upsampler that robustly restores high-resolution spatial details from coarse features.<n>We also present a simple yet effective Global Bias Alleviation operation to subtract the inherent global context from patch features.
  arXiv  Detail & Related papers  (2025-08-25T14:22:57Z)
• Open-Vocabulary Semantic Segmentation with Image Embedding Balancing [33.69721994194684]
  We propose a novel framework for openvocabulary semantic segmentation called EBSeg. AdaB Decoder is designed to generate different image embeddings for both training and new classes. SSC Loss aligns the inter-classes affinity in the image feature space with that in the text feature space of CLIP.
  arXiv  Detail & Related papers  (2024-06-14T08:34:20Z)
• EmerDiff: Emerging Pixel-level Semantic Knowledge in Diffusion Models [52.3015009878545]
  We develop an image segmentor capable of generating fine-grained segmentation maps without any additional training. Our framework identifies semantic correspondences between image pixels and spatial locations of low-dimensional feature maps. In extensive experiments, the produced segmentation maps are demonstrated to be well delineated and capture detailed parts of the images.
  arXiv  Detail & Related papers  (2024-01-22T07:34:06Z)
• Learning Semantic Segmentation with Query Points Supervision on Aerial Images [57.09251327650334]
  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.
  arXiv  Detail & Related papers  (2023-09-11T14:32:04Z)
• 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)
• Learning to Annotate Part Segmentation with Gradient Matching [58.100715754135685]
  This paper focuses on tackling semi-supervised part segmentation tasks by generating high-quality images with a pre-trained GAN. In particular, we formulate the annotator learning as a learning-to-learn problem. We show that our method can learn annotators from a broad range of labelled images including real images, generated images, and even analytically rendered images.
  arXiv  Detail & Related papers  (2022-11-06T01:29:22Z)
• 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)
• Semi-supervised Semantic Segmentation with Directional Context-aware Consistency [66.49995436833667]
  We focus on the semi-supervised segmentation problem where only a small set of labeled data is provided with a much larger collection of totally unlabeled images. A preferred high-level representation should capture the contextual information while not losing self-awareness. We present the Directional Contrastive Loss (DC Loss) to accomplish the consistency in a pixel-to-pixel manner.
  arXiv  Detail & Related papers  (2021-06-27T03:42:40Z)
• All you need are a few pixels: semantic segmentation with PixelPick [30.234492042103966]
  In this work, we show that in order to achieve a good level of segmentation performance, all you need are a few well-chosen pixel labels. We demonstrate how to exploit this phenomena within an active learning framework, termed PixelPick, to radically reduce labelling cost.
  arXiv  Detail & Related papers  (2021-04-13T17:55:33Z)
• 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)
• Few-Shot Semantic Segmentation Augmented with Image-Level Weak Annotations [23.02986307143718]
  Recent progress in fewshot semantic segmentation tackles the issue by only a few pixel-level annotated examples. Our key idea is to learn a better prototype representation of the class by fusing the knowledge from the image-level labeled data. We propose a new framework, called PAIA, to learn the class prototype representation in a metric space by integrating image-level annotations.
  arXiv  Detail & Related papers  (2020-07-03T04:58:20Z)

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.