Combi-CAM: A Novel Multi-Layer Approach for Explainable Image Geolocalization

• URL: http://arxiv.org/abs/2603.24117v1
• Date: Wed, 25 Mar 2026 09:29:02 GMT
• Title: Combi-CAM: A Novel Multi-Layer Approach for Explainable Image Geolocalization
• Abstract summary: We present Combi-CAM, a novel method that enhances the explainability of CNN-based geolocalization models.<n>This approach provides a more detailed understanding of how different image features contribute to the model's decisions.
• Score: 0.5461938536945722
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Planet-scale photo geolocalization involves the intricate task of estimating the geographic location depicted in an image purely based on its visual features. While deep learning models, particularly convolutional neural networks (CNNs), have significantly advanced this field, understanding the reasoning behind their predictions remains challenging. In this paper, we present Combi-CAM, a novel method that enhances the explainability of CNN-based geolocalization models by combining gradient-weighted class activation maps obtained from several layers of the network architecture, rather than using only information from the deepest layer as is typically done. This approach provides a more detailed understanding of how different image features contribute to the model's decisions, offering deeper insights than the traditional approaches.

Related papers

• Learning Entropy and Spatial Adaptation Dynamics of Multilayer Perceptrons for Structural Point Extraction [1.4681690787310104]
  This paper extends the concept of Learning Entropy (LE) from temporal adaptive systems to spatial learning in multilayer spatial networks (MLPs)<n>An is trained to predict the intensity of a center pixel from its surrounding spatial context, while LE is evaluated from the incremental adaptation of neural weights during learning across image-derived samples.<n>The Spatial Learning Entropy Maps (SLEM) identify unusual image points and regions that induce strong adaptation of the neural network and therefore have an important role in the learning process.
  arXiv  Detail & Related papers  (2026-06-08T21:05:03Z)
• GCRPNet: Graph-Enhanced Contextual and Regional Perception Network for Salient Object Detection in Optical Remote Sensing Images [68.33481681452675]
  We propose a graph-enhanced contextual and regional perception network (GCRPNet)<n>It builds upon the Mamba architecture to simultaneously capture long-range dependencies and enhance regional feature representation.<n>It performs adaptive patch scanning on feature maps processed via multi-scale convolutions, thereby capturing rich local region information.
  arXiv  Detail & Related papers  (2025-08-14T11:31:43Z)
• Convolutional Learning on Multigraphs [153.20329791008095]
  We develop convolutional information processing on multigraphs and introduce convolutional multigraph neural networks (MGNNs) To capture the complex dynamics of information diffusion within and across each of the multigraph's classes of edges, we formalize a convolutional signal processing model. We develop a multigraph learning architecture, including a sampling procedure to reduce computational complexity. The introduced architecture is applied towards optimal wireless resource allocation and a hate speech localization task, offering improved performance over traditional graph neural networks.
  arXiv  Detail & Related papers  (2022-09-23T00:33:04Z)
• Shap-CAM: Visual Explanations for Convolutional Neural Networks based on Shapley Value [86.69600830581912]
  We develop a novel visual explanation method called Shap-CAM based on class activation mapping. We demonstrate that Shap-CAM achieves better visual performance and fairness for interpreting the decision making process.
  arXiv  Detail & Related papers  (2022-08-07T00:59:23Z)
• Enhancing Deep Neural Network Saliency Visualizations with Gradual Extrapolation [0.0]
  We propose an enhancement technique of the Class Activation Mapping methods like Grad-CAM or Excitation Backpropagation. Our idea, called Gradual Extrapolation, can supplement any method that generates a heatmap picture by sharpening the output.
  arXiv  Detail & Related papers  (2021-04-11T07:39:35Z)
• Spatio-Temporal Inception Graph Convolutional Networks for Skeleton-Based Action Recognition [126.51241919472356]
  We design a simple and highly modularized graph convolutional network architecture for skeleton-based action recognition. Our network is constructed by repeating a building block that aggregates multi-granularity information from both the spatial and temporal paths.
  arXiv  Detail & Related papers  (2020-11-26T14:43:04Z)
• Multi-Level Graph Convolutional Network with Automatic Graph Learning for Hyperspectral Image Classification [63.56018768401328]
  We propose a Multi-level Graph Convolutional Network (GCN) with Automatic Graph Learning method (MGCN-AGL) for HSI classification. By employing attention mechanism to characterize the importance among spatially neighboring regions, the most relevant information can be adaptively incorporated to make decisions. Our MGCN-AGL encodes the long range dependencies among image regions based on the expressive representations that have been produced at local level.
  arXiv  Detail & Related papers  (2020-09-19T09:26:20Z)
• Learning Local Complex Features using Randomized Neural Networks for Texture Analysis [0.1474723404975345]
  We present a new approach that combines a learning technique and the Complex Network (CN) theory for texture analysis. This method takes advantage of the representation capacity of CN to model a texture image as a directed network. This neural network has a single hidden layer and uses a fast learning algorithm, which is able to learn local CN patterns for texture characterization.
  arXiv  Detail & Related papers  (2020-07-10T23:18:01Z)
• Seismic horizon detection with neural networks [62.997667081978825]
  This paper is an open-sourced research of applying binary segmentation approach to the task of horizon detection on multiple real seismic cubes with a focus on inter-cube generalization of the predictive model. The main contribution of this paper is an open-sourced research of applying binary segmentation approach to the task of horizon detection on multiple real seismic cubes with a focus on inter-cube generalization of the predictive model.
  arXiv  Detail & Related papers  (2020-01-10T11:30:50Z)
• Contextual Encoder-Decoder Network for Visual Saliency Prediction [42.047816176307066]
  We propose an approach based on a convolutional neural network pre-trained on a large-scale image classification task. We combine the resulting representations with global scene information for accurately predicting visual saliency. Compared to state of the art approaches, the network is based on a lightweight image classification backbone.
  arXiv  Detail & Related papers  (2019-02-18T16:15:25Z)

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.