Sequence-Based Filtering for Visual Route-Based Navigation: Analysing the Benefits, Trade-offs and Design Choices

• URL: http://arxiv.org/abs/2103.01994v1
• Date: Tue, 2 Mar 2021 19:24:58 GMT
• Title: Sequence-Based Filtering for Visual Route-Based Navigation: Analysing the Benefits, Trade-offs and Design Choices
• Authors: Mihnea-Alexandru Tomit\u{a}, Mubariz Zaffar, Michael Milford, Klaus McDonald-Maier, Shoaib Ehsan
• Abstract summary: An emerging trend in Visual Place Recognition (VPR) is the use of sequence-based filtering methods on top of single-frame-based place matching techniques. This paper conducts an in-depth investigation of the relationship between the performance of single-frame-based place matching techniques and the use of sequence-based filtering on top of those methods.
• Score: 17.48671856442762
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual Place Recognition (VPR) is the ability to correctly recall a previously visited place using visual information under environmental, viewpoint and appearance changes. An emerging trend in VPR is the use of sequence-based filtering methods on top of single-frame-based place matching techniques for route-based navigation. The combination leads to varying levels of potential place matching performance boosts at increased computational costs. This raises a number of interesting research questions: How does performance boost (due to sequential filtering) vary along the entire spectrum of single-frame-based matching methods? How does sequence matching length affect the performance curve? Which specific combinations provide a good trade-off between performance and computation? However, there is lack of previous work looking at these important questions and most of the sequence-based filtering work to date has been used without a systematic approach. To bridge this research gap, this paper conducts an in-depth investigation of the relationship between the performance of single-frame-based place matching techniques and the use of sequence-based filtering on top of those methods. It analyzes individual trade-offs, properties and limitations for different combinations of single-frame-based and sequential techniques. A number of state-of-the-art VPR methods and widely used public datasets are utilized to present the findings that contain a number of meaningful insights for the VPR community.

Related papers

• GraphTransfer: A Generic Feature Fusion Framework for Collaborative Filtering [23.359028687426925]
  We present GraphTransfer, a simple but universal feature fusion framework for GNN-based collaborative filtering. Our method accurately fuses different types of features by first extracting graph features from the user-item interaction graph and auxiliary features from users and items using GNN. Theoretical analysis and experiments on public datasets show that GraphTransfer outperforms other feature fusion methods in CF tasks.
  arXiv  Detail & Related papers  (2024-08-11T14:47:34Z)
• A Refreshed Similarity-based Upsampler for Direct High-Ratio Feature Upsampling [54.05517338122698]
  We propose an explicitly controllable query-key feature alignment from both semantic-aware and detail-aware perspectives. We also develop a fine-grained neighbor selection strategy on HR features, which is simple yet effective for alleviating mosaic artifacts. Our proposed ReSFU framework consistently achieves satisfactory performance on different segmentation applications.
  arXiv  Detail & Related papers  (2024-07-02T14:12:21Z)
• Deep Frequency-Aware Functional Maps for Robust Shape Matching [17.276162941967943]
  We propose a novel unsupervised learning-based framework called Deep Frequency-Aware Functional Maps. We first introduce a general constraint called Spectral Filter Operator Preservation to compute desirable functional maps. We then directly utilize the proposed constraint as a loss function to supervise functional maps, pointwise maps, and filter functions simultaneously.
  arXiv  Detail & Related papers  (2024-02-06T11:16:18Z)
• Multi-Technique Sequential Information Consistency For Dynamic Visual Place Recognition In Changing Environments [23.33092172788319]
  Visual place recognition (VPR) is an essential component of robot navigation and localization systems. No single VPR technique excels in every environmental condition. We propose a VPR system dubbed Multi-Sequential Information Consistency (MuSIC)
  arXiv  Detail & Related papers  (2024-01-16T10:35:01Z)
• A-MuSIC: An Adaptive Ensemble System For Visual Place Recognition In Changing Environments [22.58641358408613]
  Visual place recognition (VPR) is an essential component of robot navigation and localization systems. No single VPR technique excels in every environmental condition. adaptive VPR system dubbed Adaptive Multi-Self Identification and Correction (A-MuSIC) A-MuSIC matches or beats state-of-the-art VPR performance across all tested benchmark datasets.
  arXiv  Detail & Related papers  (2023-03-24T19:25:22Z)
• Learning Versatile Convolution Filters for Efficient Visual Recognition [125.34595948003745]
  This paper introduces versatile filters to construct efficient convolutional neural networks. We conduct theoretical analysis on network complexity and an efficient convolution scheme is introduced. Experimental results on benchmark datasets and neural networks demonstrate that our versatile filters are able to achieve comparable accuracy as that of original filters.
  arXiv  Detail & Related papers  (2021-09-20T06:07:14Z)
• Adversarial Feature Augmentation and Normalization for Visual Recognition [109.6834687220478]
  Recent advances in computer vision take advantage of adversarial data augmentation to ameliorate the generalization ability of classification models. Here, we present an effective and efficient alternative that advocates adversarial augmentation on intermediate feature embeddings. We validate the proposed approach across diverse visual recognition tasks with representative backbone networks.
  arXiv  Detail & Related papers  (2021-03-22T20:36:34Z)
• Infinite Feature Selection: A Graph-based Feature Filtering Approach [78.63188057505012]
  We propose a filtering feature selection framework that considers subsets of features as paths in a graph. Going to infinite allows to constrain the computational complexity of the selection process. We show that Inf-FS behaves better in almost any situation, that is, when the number of features to keep are fixed a priori.
  arXiv  Detail & Related papers  (2020-06-15T07:20:40Z)
• Dependency Aware Filter Pruning [74.69495455411987]
  Pruning a proportion of unimportant filters is an efficient way to mitigate the inference cost. Previous work prunes filters according to their weight norms or the corresponding batch-norm scaling factors. We propose a novel mechanism to dynamically control the sparsity-inducing regularization so as to achieve the desired sparsity.
  arXiv  Detail & Related papers  (2020-05-06T07:41:22Z)
• Image Matching across Wide Baselines: From Paper to Practice [80.9424750998559]
  We introduce a comprehensive benchmark for local features and robust estimation algorithms. Our pipeline's modular structure allows easy integration, configuration, and combination of different methods. We show that with proper settings, classical solutions may still outperform the perceived state of the art.
  arXiv  Detail & Related papers  (2020-03-03T15:20:57Z)

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.