Frugal Learning of Virtual Exemplars for Label-Efficient Satellite Image Change Detection

• URL: http://arxiv.org/abs/2203.11559v1
• Date: Tue, 22 Mar 2022 09:29:42 GMT
• Title: Frugal Learning of Virtual Exemplars for Label-Efficient Satellite Image Change Detection
• Authors: Hichem Sahbi, Sebastien Deschamps
• Abstract summary: In this paper, we devise a novel interactive satellite image change detection algorithm based on active learning. The proposed framework is iterative and relies on a question and answer model which asks the oracle (user) questions about the most informative display. The contribution of our framework resides in a novel display model which selects the most representative and diverse virtual exemplars.
• Score: 12.18340575383456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we devise a novel interactive satellite image change detection algorithm based on active learning. The proposed framework is iterative and relies on a question and answer model which asks the oracle (user) questions about the most informative display (subset of critical images), and according to the user's responses, updates change detections. The contribution of our framework resides in a novel display model which selects the most representative and diverse virtual exemplars that adversely challenge the learned change detection functions, thereby leading to highly discriminating functions in the subsequent iterations of active learning. Extensive experiments, conducted on the challenging task of interactive satellite image change detection, show the superiority of the proposed virtual display model against the related work.

Related papers

• Chain-of-Spot: Interactive Reasoning Improves Large Vision-Language Models [81.71651422951074]
  Chain-of-Spot (CoS) method is a novel approach that enhances feature extraction by focusing on key regions of interest. This technique allows LVLMs to access more detailed visual information without altering the original image resolution. Our empirical findings demonstrate a significant improvement in LVLMs' ability to understand and reason about visual content.
  arXiv  Detail & Related papers  (2024-03-19T17:59:52Z)
• Visual In-Context Learning for Large Vision-Language Models [62.5507897575317]
  In Large Visual Language Models (LVLMs) the efficacy of In-Context Learning (ICL) remains limited by challenges in cross-modal interactions and representation disparities. We introduce a novel Visual In-Context Learning (VICL) method comprising Visual Demonstration Retrieval, Intent-Oriented Image Summarization, and Intent-Oriented Demonstration Composition. Our approach retrieves images via ''Retrieval & Rerank'' paradigm, summarises images with task intent and task-specific visual parsing, and composes language-based demonstrations.
  arXiv  Detail & Related papers  (2024-02-18T12:43:38Z)
• Reinforcement-based Display-size Selection for Frugal Satellite Image Change Detection [5.656581242851759]
  We introduce a novel interactive satellite image change detection algorithm based on active learning. The proposed method is iterative and consists in frugally probing the user (oracle) about the labels of the most critical images.
  arXiv  Detail & Related papers  (2023-12-28T11:14:43Z)
• Frugal Satellite Image Change Detection with Deep-Net Inversion [5.656581242851759]
  We devise a novel algorithm for change detection based on active learning. The proposed method is based on a question and answer model that probes an oracle (user) about the relevance of changes. The main contribution resides in a novel adversarial model that allows learning the most representative, diverse and uncertain virtual exemplars.
  arXiv  Detail & Related papers  (2023-09-26T09:25:53Z)
• Adversarial Virtual Exemplar Learning for Label-Frugal Satellite Image Change Detection [12.18340575383456]
  In this paper, we investigate satellite image change detection using active learning. Our method is interactive and relies on a question and answer model which asks the oracle (user) questions about the most informative display. The main contribution of our method consists in a novel adversarial model that allows frugally probing the oracle with only the most representative, diverse and uncertain virtual exemplars.
  arXiv  Detail & Related papers  (2022-12-28T17:46:20Z)
• Visual Perturbation-aware Collaborative Learning for Overcoming the Language Prior Problem [60.0878532426877]
  We propose a novel collaborative learning scheme from the viewpoint of visual perturbation calibration. Specifically, we devise a visual controller to construct two sorts of curated images with different perturbation extents. The experimental results on two diagnostic VQA-CP benchmark datasets evidently demonstrate its effectiveness.
  arXiv  Detail & Related papers  (2022-07-24T23:50:52Z)
• Reinforcement-based frugal learning for satellite image change detection [12.18340575383456]
  We introduce a novel interactive satellite image change detection algorithm based on active learning. The proposed approach is iterative and asks the user (oracle) questions about the targeted changes. We consider a probabilistic framework which assigns to each unlabeled sample a relevance measure modeling how critical is that sample when training change detection functions.
  arXiv  Detail & Related papers  (2022-03-22T09:37:24Z)
• Reliable Shot Identification for Complex Event Detection via Visual-Semantic Embedding [72.9370352430965]
  We propose a visual-semantic guided loss method for event detection in videos. Motivated by curriculum learning, we introduce a negative elastic regularization term to start training the classifier with instances of high reliability. An alternative optimization algorithm is developed to solve the proposed challenging non-net regularization problem.
  arXiv  Detail & Related papers  (2021-10-12T11:46:56Z)
• Active learning for interactive satellite image change detection [12.907324263748817]
  We introduce in this paper a novel active learning algorithm for satellite image change detection. The proposed solution is interactive and based on a question and answer model, which asks an oracle about the relevance of sampled satellite image pairs. Experiments on the task of satellite image change detection after natural hazards (namely tornadoes) show the relevance of the proposed method against the related work.
  arXiv  Detail & Related papers  (2021-10-08T16:59:12Z)
• Visual Adversarial Imitation Learning using Variational Models [60.69745540036375]
  Reward function specification remains a major impediment for learning behaviors through deep reinforcement learning. Visual demonstrations of desired behaviors often presents an easier and more natural way to teach agents. We develop a variational model-based adversarial imitation learning algorithm.
  arXiv  Detail & Related papers  (2021-07-16T00:15:18Z)
• Self-Supervised Representation Learning from Flow Equivariance [97.13056332559526]
  We present a new self-supervised learning representation framework that can be directly deployed on a video stream of complex scenes. Our representations, learned from high-resolution raw video, can be readily used for downstream tasks on static images.
  arXiv  Detail & Related papers  (2021-01-16T23:44:09Z)

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.