Semantic Interleaving Global Channel Attention for Multilabel Remote Sensing Image Classification

• URL: http://arxiv.org/abs/2208.02613v1
• Date: Thu, 4 Aug 2022 12:28:04 GMT
• Title: Semantic Interleaving Global Channel Attention for Multilabel Remote Sensing Image Classification
• Authors: Yongkun Liu, Kesong Ni, Yuhan Zhang, Lijian Zhou and Kun Zhao
• Abstract summary: Multi-Label Remote Sensing Image Classification (MLRSIC) has received increasing research interest. Taking the cooccurrence relationship of multiple labels as additional information helps to improve the performance of this task. Current methods do not make full use of label correlation to form feature representation. In this paper, a novel method called Semantic Interleaving Global Channel Attention (SIGNA) is proposed for MLRSIC.
• Score: 6.52523058108343
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-Label Remote Sensing Image Classification (MLRSIC) has received increasing research interest. Taking the cooccurrence relationship of multiple labels as additional information helps to improve the performance of this task. Current methods focus on using it to constrain the final feature output of a Convolutional Neural Network (CNN). On the one hand, these methods do not make full use of label correlation to form feature representation. On the other hand, they increase the label noise sensitivity of the system, resulting in poor robustness. In this paper, a novel method called Semantic Interleaving Global Channel Attention (SIGNA) is proposed for MLRSIC. First, the label co-occurrence graph is obtained according to the statistical information of the data set. The label co-occurrence graph is used as the input of the Graph Neural Network (GNN) to generate optimal feature representations. Then, the semantic features and visual features are interleaved, to guide the feature expression of the image from the original feature space to the semantic feature space with embedded label relations. SIGNA triggers global attention of feature maps channels in a new semantic feature space to extract more important visual features. Multihead SIGNA based feature adaptive weighting networks are proposed to act on any layer of CNN in a plug-and-play manner. For remote sensing images, better classification performance can be achieved by inserting CNN into the shallow layer. We conduct extensive experimental comparisons on three data sets: UCM data set, AID data set, and DFC15 data set. Experimental results demonstrate that the proposed SIGNA achieves superior classification performance compared to state-of-the-art (SOTA) methods. It is worth mentioning that the codes of this paper will be open to the community for reproducibility research. Our codes are available at https://github.com/kyle-one/SIGNA.

Related papers

• Semantic-Spatial Feature Fusion with Dynamic Graph Refinement for Remote Sensing Image Captioning [11.015244501780078]
  This paper presents a semantic-spatial feature fusion with dynamic graph refinement (SFDR) method. The proposed SFDR method significantly enhances the quality of the generated descriptions. Experimental results on three benchmark datasets demonstrate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2025-03-30T14:14:41Z)
• MFGNet: Dynamic Modality-Aware Filter Generation for RGB-T Tracking [72.65494220685525]
  We propose a new dynamic modality-aware filter generation module (named MFGNet) to boost the message communication between visible and thermal data. We generate dynamic modality-aware filters with two independent networks. The visible and thermal filters will be used to conduct a dynamic convolutional operation on their corresponding input feature maps respectively. To address issues caused by heavy occlusion, fast motion, and out-of-view, we propose to conduct a joint local and global search by exploiting a new direction-aware target-driven attention mechanism.
  arXiv  Detail & Related papers  (2021-07-22T03:10:51Z)
• Attention-Driven Dynamic Graph Convolutional Network for Multi-Label Image Recognition [53.17837649440601]
  We propose an Attention-Driven Dynamic Graph Convolutional Network (ADD-GCN) to dynamically generate a specific graph for each image. Experiments on public multi-label benchmarks demonstrate the effectiveness of our method.
  arXiv  Detail & Related papers  (2020-12-05T10:10:12Z)
• Knowledge-Guided Multi-Label Few-Shot Learning for General Image Recognition [75.44233392355711]
  KGGR framework exploits prior knowledge of statistical label correlations with deep neural networks. It first builds a structured knowledge graph to correlate different labels based on statistical label co-occurrence. Then, it introduces the label semantics to guide learning semantic-specific features. It exploits a graph propagation network to explore graph node interactions.
  arXiv  Detail & Related papers  (2020-09-20T15:05:29Z)
• Instance-Aware Graph Convolutional Network for Multi-Label Classification [55.131166957803345]
  Graph convolutional neural network (GCN) has effectively boosted the multi-label image recognition task. We propose an instance-aware graph convolutional neural network (IA-GCN) framework for multi-label classification.
  arXiv  Detail & Related papers  (2020-08-19T12:49:28Z)
• Sequential Graph Convolutional Network for Active Learning [53.99104862192055]
  We propose a novel pool-based Active Learning framework constructed on a sequential Graph Convolution Network (GCN) With a small number of randomly sampled images as seed labelled examples, we learn the parameters of the graph to distinguish labelled vs unlabelled nodes. We exploit these characteristics of GCN to select the unlabelled examples which are sufficiently different from labelled ones.
  arXiv  Detail & Related papers  (2020-06-18T00:55:10Z)
• Multi-Label Text Classification using Attention-based Graph Neural Network [0.0]
  A graph attention network-based model is proposed to capture the attentive dependency structure among the labels. The proposed model achieves similar or better performance compared to the previous state-of-the-art models.
  arXiv  Detail & Related papers  (2020-03-22T17:12:43Z)
• High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification [84.43394420267794]
  We propose a novel framework by learning high-order relation and topology information for discriminative features and robust alignment. Our framework significantly outperforms state-of-the-art by6.5%mAP scores on Occluded-Duke dataset.
  arXiv  Detail & Related papers  (2020-03-18T12:18:35Z)

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.