Deep Active Learning for Multi-Label Classification of Remote Sensing Images

• URL: http://arxiv.org/abs/2212.01165v3
• Date: Mon, 25 Sep 2023 09:13:07 GMT
• Title: Deep Active Learning for Multi-Label Classification of Remote Sensing Images
• Abstract summary: We introduce deep active learning (AL) for multi-label classification problems in remote sensing (RS) In particular, we investigate the effectiveness of several AL query functions for MLC of RS images. All the considered query functions are introduced for the first time in the framework of MLC problems in RS.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this letter, we introduce deep active learning (AL) for multi-label classification (MLC) problems in remote sensing (RS). In particular, we investigate the effectiveness of several AL query functions for MLC of RS images. Unlike the existing AL query functions (which are defined for single-label classification or semantic segmentation problems), each query function in this paper is based on the evaluation of two criteria: i) multi-label uncertainty; and ii) multi-label diversity. The multi-label uncertainty criterion is associated to the confidence of the deep neural networks (DNNs) in correctly assigning multi-labels to each image. To assess this criterion, we investigate three strategies: i) learning multi-label loss ordering; ii) measuring temporal discrepancy of multi-label predictions; and iii) measuring magnitude of approximated gradient embeddings. The multi-label diversity criterion is associated to the selection of a set of images that are as diverse as possible to each other that prevents redundancy among them. To assess this criterion, we exploit a clustering based strategy. We combine each of the above-mentioned uncertainty strategies with the clustering based diversity strategy, resulting in three different query functions. All the considered query functions are introduced for the first time in the framework of MLC problems in RS. Experimental results obtained on two benchmark archives show that these query functions result in the selection of a highly informative set of samples at each iteration of the AL process.

Related papers

• Class-Independent Increment: An Efficient Approach for Multi-label Class-Incremental Learning [49.65841002338575]
  This paper focuses on the challenging yet practical multi-label class-incremental learning (MLCIL) problem. We propose a novel class-independent incremental network (CINet) to extract multiple class-level embeddings for multi-label samples. It learns and preserves the knowledge of different classes by constructing class-specific tokens.
  arXiv  Detail & Related papers  (2025-03-01T14:40:52Z)
• Dynamic Correlation Learning and Regularization for Multi-Label Confidence Calibration [60.95748658638956]
  This paper introduces the Multi-Label Confidence task, aiming to provide well-calibrated confidence scores in multi-label scenarios. Existing single-label calibration methods fail to account for category correlations, which are crucial for addressing semantic confusion. We propose the Dynamic Correlation Learning and Regularization algorithm, which leverages multi-grained semantic correlations to better model semantic confusion.
  arXiv  Detail & Related papers  (2024-07-09T13:26:21Z)
• Multi-Label Feature Selection Using Adaptive and Transformed Relevance [0.0]
  This paper presents a novel information-theoretical filter-based multi-label feature selection, called ATR, with a new function. ATR ranks features considering individual labels as well as abstract label space discriminative powers. Our experiments affirm the scalability of ATR for benchmarks characterized by extensive feature and label spaces.
  arXiv  Detail & Related papers  (2023-09-26T09:01:38Z)
• Heterogeneous Semantic Transfer for Multi-label Recognition with Partial Labels [70.45813147115126]
  Multi-label image recognition with partial labels (MLR-PL) may greatly reduce the cost of annotation and thus facilitate large-scale MLR. We find that strong semantic correlations exist within each image and across different images. These correlations can help transfer the knowledge possessed by the known labels to retrieve the unknown labels.
  arXiv  Detail & Related papers  (2022-05-23T08:37:38Z)
• Decision Making for Hierarchical Multi-label Classification with Multidimensional Local Precision Rate [4.812468844362369]
  We introduce a new statistic called the multidimensional local precision rate (mLPR) for each object in each class. We show that classification decisions made by simply sorting objects across classes in descending order of their mLPRs can, in theory, ensure the class hierarchy. In response, we introduce HierRank, a new algorithm that maximizes an empirical version of CATCH using estimated mLPRs while respecting the hierarchy.
  arXiv  Detail & Related papers  (2022-05-16T17:43:35Z)
• Learning Self-Supervised Low-Rank Network for Single-Stage Weakly and Semi-Supervised Semantic Segmentation [119.009033745244]
  This paper presents a Self-supervised Low-Rank Network ( SLRNet) for single-stage weakly supervised semantic segmentation (WSSS) and semi-supervised semantic segmentation (SSSS) SLRNet uses cross-view self-supervision, that is, it simultaneously predicts several attentive LR representations from different views of an image to learn precise pseudo-labels. Experiments on the Pascal VOC 2012, COCO, and L2ID datasets demonstrate that our SLRNet outperforms both state-of-the-art WSSS and SSSS methods with a variety of different settings.
  arXiv  Detail & Related papers  (2022-03-19T09:19:55Z)
• A Novel Triplet Sampling Method for Multi-Label Remote Sensing Image Search and Retrieval [1.123376893295777]
  A common approach for learning the metric space relies on the selection of triplets of similar (positive) and dissimilar (negative) images. We propose a novel triplet sampling method in the framework of deep neural networks (DNNs) defined for multi-label RS CBIR problems.
  arXiv  Detail & Related papers  (2021-05-08T09:16:09Z)
• Rank-Consistency Deep Hashing for Scalable Multi-Label Image Search [90.30623718137244]
  We propose a novel deep hashing method for scalable multi-label image search. A new rank-consistency objective is applied to align the similarity orders from two spaces. A powerful loss function is designed to penalize the samples whose semantic similarity and hamming distance are mismatched.
  arXiv  Detail & Related papers  (2021-02-02T13:46:58Z)
• Multi-scale Adaptive Task Attention Network for Few-Shot Learning [5.861206243996454]
  The goal of few-shot learning is to classify unseen categories with few labeled samples. This paper proposes a novel Multi-scale Adaptive Task Attention Network (MATANet) for few-shot learning.
  arXiv  Detail & Related papers  (2020-11-30T00:36:01Z)
• SPL-MLL: Selecting Predictable Landmarks for Multi-Label Learning [87.27700889147144]
  We propose to select a small subset of labels as landmarks which are easy to predict according to input (predictable) and can well recover the other possible labels (representative) We employ the Alternating Direction Method (ADM) to solve our problem. Empirical studies on real-world datasets show that our method achieves superior classification performance over other state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-08-16T11:07:44Z)
• Joint Visual and Temporal Consistency for Unsupervised Domain Adaptive Person Re-Identification [64.37745443119942]
  This paper jointly enforces visual and temporal consistency in the combination of a local one-hot classification and a global multi-class classification. Experimental results on three large-scale ReID datasets demonstrate the superiority of proposed method in both unsupervised and unsupervised domain adaptive ReID tasks.
  arXiv  Detail & Related papers  (2020-07-21T14:31:27Z)

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.