A Complementarity-Based Switch-Fuse System for Improved Visual Place
Recognition
- URL: http://arxiv.org/abs/2303.00714v1
- Date: Wed, 1 Mar 2023 18:19:10 GMT
- Title: A Complementarity-Based Switch-Fuse System for Improved Visual Place
Recognition
- Authors: Maria Waheed, Sania Waheed, Michael Milford, Klaus McDonald-Maier and
Shoaib Ehsan
- Abstract summary: Switch-Fuse is an interesting way to combine the robustness of switching VPR techniques based on complementarity and the force of fusing the carefully selected techniques to significantly improve performance.
The system combines two significant processes, switching and fusing VPR techniques, which together as a hybrid model substantially improve performance on all major VPR data sets illustrated using PR curves.
- Score: 19.14779092252812
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recently several fusion and switching based approaches have been presented to
solve the problem of Visual Place Recognition. In spite of these systems
demonstrating significant boost in VPR performance they each have their own set
of limitations. The multi-process fusion systems usually involve employing
brute force and running all available VPR techniques simultaneously while the
switching method attempts to negate this practise by only selecting the best
suited VPR technique for given query image. But switching does fail at times
when no available suitable technique can be identified. An innovative solution
would be an amalgamation of the two otherwise discrete approaches to combine
their competitive advantages while negating their shortcomings. The proposed,
Switch-Fuse system, is an interesting way to combine both the robustness of
switching VPR techniques based on complementarity and the force of fusing the
carefully selected techniques to significantly improve performance. Our system
holds a structure superior to the basic fusion methods as instead of simply
fusing all or any random techniques, it is structured to first select the best
possible VPR techniques for fusion, according to the query image. The system
combines two significant processes, switching and fusing VPR techniques, which
together as a hybrid model substantially improve performance on all major VPR
data sets illustrated using PR curves.
Related papers
- Faster Optimal Coalition Structure Generation via Offline Coalition Selection and Graph-Based Search [61.08720171136229]
We present a novel algorithm, SMART, for the problem based on a hybridization of three innovative techniques.
Two of these techniques are based on dynamic programming, where we show a powerful connection between the coalitions selected for evaluation and the performance of the algorithms.
Our techniques bring a new way of approaching the problem and a new level of precision to the field.
arXiv Detail & Related papers (2024-07-22T23:24:03Z) - UAV-enabled Collaborative Beamforming via Multi-Agent Deep Reinforcement Learning [79.16150966434299]
We formulate a UAV-enabled collaborative beamforming multi-objective optimization problem (UCBMOP) to maximize the transmission rate of the UVAA and minimize the energy consumption of all UAVs.
We use the heterogeneous-agent trust region policy optimization (HATRPO) as the basic framework, and then propose an improved HATRPO algorithm, namely HATRPO-UCB.
arXiv Detail & Related papers (2024-04-11T03:19:22Z) - Once for Both: Single Stage of Importance and Sparsity Search for Vision Transformer Compression [63.23578860867408]
We investigate how to integrate the evaluations of importance and sparsity scores into a single stage.
We present OFB, a cost-efficient approach that simultaneously evaluates both importance and sparsity scores.
Experiments demonstrate that OFB can achieve superior compression performance over state-of-the-art searching-based and pruning-based methods.
arXiv Detail & Related papers (2024-03-23T13:22:36Z) - Transforming Image Super-Resolution: A ConvFormer-based Efficient Approach [58.57026686186709]
We introduce the Convolutional Transformer layer (ConvFormer) and propose a ConvFormer-based Super-Resolution network (CFSR)
CFSR inherits the advantages of both convolution-based and transformer-based approaches.
Experiments demonstrate that CFSR strikes an optimal balance between computational cost and performance.
arXiv Detail & Related papers (2024-01-11T03:08:00Z) - 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) - Boosting Performance of a Baseline Visual Place Recognition Technique by
Predicting the Maximally Complementary Technique [25.916992891359055]
One recent promising approach to the Visual Place Recognition problem has been to fuse the place recognition estimates of multiple complementary VPR techniques.
These approaches require all potential VPR methods to be brute-force run before they are selectively fused.
Here we propose an alternative approach that instead starts with a known single base VPR technique, and learns to predict the most complementary additional VPR technique to fuse with it.
arXiv Detail & Related papers (2022-10-14T04:32:23Z) - SwitchHit: A Probabilistic, Complementarity-Based Switching System for
Improved Visual Place Recognition in Changing Environments [20.917586014941033]
There is no universal VPR technique that can work in all types of environments.
Running multiple VPR techniques in parallel may be prohibitive for resource-constrained embedded platforms.
This paper presents a probabilistic complementarity based switching VPR system, SwitchHit.
arXiv Detail & Related papers (2022-03-01T16:23:22Z) - Low-light Image Enhancement by Retinex Based Algorithm Unrolling and
Adjustment [50.13230641857892]
We propose a new deep learning framework for the low-light image enhancement (LIE) problem.
The proposed framework contains a decomposition network inspired by algorithm unrolling, and adjustment networks considering both global brightness and local brightness sensitivity.
Experiments on a series of typical LIE datasets demonstrated the effectiveness of the proposed method, both quantitatively and visually, as compared with existing methods.
arXiv Detail & Related papers (2022-02-12T03:59:38Z) - Unsupervised Complementary-aware Multi-process Fusion for Visual Place
Recognition [28.235055888073855]
We propose an unsupervised algorithm that finds the most robust set of VPR techniques to use in the current deployment environment.
The proposed dynamic multi-process fusion (Dyn-MPF) has superior VPR performance compared to a variety of challenging competitive methods.
arXiv Detail & Related papers (2021-12-09T04:57:33Z) - Light Field Saliency Detection with Dual Local Graph Learning
andReciprocative Guidance [148.9832328803202]
We model the infor-mation fusion within focal stack via graph networks.
We build a novel dual graph modelto guide the focal stack fusion process using all-focus pat-terns.
arXiv Detail & Related papers (2021-10-02T00:54:39Z) - Improving Visual Place Recognition Performance by Maximising
Complementarity [22.37892767050086]
This paper investigates the complementarity of state-of-the-art VPR methods systematically for the first time.
It identifies those combinations which can result in better performance.
Results are presented for eight state-of-the-art VPR methods on ten widely-used VPR datasets.
arXiv Detail & Related papers (2021-02-16T19:18:33Z)
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.