Exploring Object-Aware Attention Guided Frame Association for RGB-D SLAM

• URL: http://arxiv.org/abs/2510.26131v1
• Date: Thu, 30 Oct 2025 04:31:56 GMT
• Title: Exploring Object-Aware Attention Guided Frame Association for RGB-D SLAM
• Authors: Ali Caglayan, Nevrez Imamoglu, Oguzhan Guclu, Ali Osman Serhatoglu, Ahmet Burak Can, Ryosuke Nakamura,
• Abstract summary: We propose utilizing task-specific network attention for RGB-D indoor SLAM.<n>We integrate layer-wise attention information derived from network gradients with CNN feature representations to improve frame association performance.
• Score: 2.1325069168462303
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Attention models have recently emerged as a powerful approach, demonstrating significant progress in various fields. Visualization techniques, such as class activation mapping, provide visual insights into the reasoning of convolutional neural networks (CNNs). Using network gradients, it is possible to identify regions where the network pays attention during image recognition tasks. Furthermore, these gradients can be combined with CNN features to localize more generalizable, task-specific attentive (salient) regions within scenes. However, explicit use of this gradient-based attention information integrated directly into CNN representations for semantic object understanding remains limited. Such integration is particularly beneficial for visual tasks like simultaneous localization and mapping (SLAM), where CNN representations enriched with spatially attentive object locations can enhance performance. In this work, we propose utilizing task-specific network attention for RGB-D indoor SLAM. Specifically, we integrate layer-wise attention information derived from network gradients with CNN feature representations to improve frame association performance. Experimental results indicate improved performance compared to baseline methods, particularly for large environments.

Related papers

• 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)
• Referring Remote Sensing Image Segmentation with Cross-view Semantics Interaction Network [65.01521002836611]
  We propose a paralleled yet unified segmentation framework Cross-view Semantics Interaction Network (CSINet) to solve the limitations.<n>Motivated by human behavior in observing targets of interest, the network orchestrates visual cues from remote and close distances to conduct synergistic prediction.<n>In its every encoding stage, a Cross-View Window-attention module (CVWin) is utilized to supplement global and local semantics into close-view and remote-view branch features.
  arXiv  Detail & Related papers  (2025-08-02T11:57:56Z)
• Hierarchical Graph Interaction Transformer with Dynamic Token Clustering for Camouflaged Object Detection [57.883265488038134]
  We propose a hierarchical graph interaction network termed HGINet for camouflaged object detection. The network is capable of discovering imperceptible objects via effective graph interaction among the hierarchical tokenized features. Our experiments demonstrate the superior performance of HGINet compared to existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-08-27T12:53:25Z)
• ELA: Efficient Local Attention for Deep Convolutional Neural Networks [15.976475674061287]
  This paper introduces an Efficient Local Attention (ELA) method that achieves substantial performance improvements with a simple structure. To overcome these challenges, we propose the incorporation of 1D convolution and Group Normalization feature enhancement techniques. ELA can be seamlessly integrated into deep CNN networks such as ResNet, MobileNet, and DeepLab.
  arXiv  Detail & Related papers  (2024-03-02T08:06:18Z)
• Real-time Local Feature with Global Visual Information Enhancement [6.640269424085467]
  Current deep learning-based local feature algorithms always utilize convolution neural network (CNN) architecture with limited receptive field. The proposed method introduces a global enhancement module to fuse global visual clues in a light-weight network. Experiments on the public benchmarks demonstrate that the proposal can achieve considerable robustness against visual interference and meanwhile run in real time.
  arXiv  Detail & Related papers  (2022-11-20T13:44:20Z)
• Influencer Detection with Dynamic Graph Neural Networks [56.1837101824783]
  We investigate different dynamic Graph Neural Networks (GNNs) configurations for influencer detection. We show that using deep multi-head attention in GNN and encoding temporal attributes significantly improves performance.
  arXiv  Detail & Related papers  (2022-11-15T13:00:25Z)
• RGB-D SLAM Using Attention Guided Frame Association [11.484398586420067]
  We propose the use of task specific network attention for RGB-D indoor SLAM. We integrate layer-wise object attention information (layer gradients) with CNN layer representations to improve frame association performance. Experiments show promising initial results with improved performance.
  arXiv  Detail & Related papers  (2022-01-28T11:23:29Z)
• GCA-Net : Utilizing Gated Context Attention for Improving Image Forgery Localization and Detection [0.9883261192383611]
  We propose a novel Gated Context Attention Network (GCA-Net) that utilizes the non-local attention block for global context learning. We show that our method outperforms state-of-the-art networks by an average of 4.2%-5.4% AUC on multiple benchmark datasets.
  arXiv  Detail & Related papers  (2021-12-08T14:13:14Z)
• Variational Structured Attention Networks for Deep Visual Representation Learning [49.80498066480928]
  We propose a unified deep framework to jointly learn both spatial attention maps and channel attention in a principled manner. Specifically, we integrate the estimation and the interaction of the attentions within a probabilistic representation learning framework. We implement the inference rules within the neural network, thus allowing for end-to-end learning of the probabilistic and the CNN front-end parameters.
  arXiv  Detail & Related papers  (2021-03-05T07:37:24Z)
• 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)

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.