Related papers: Employing Universal Voting Schemes for Improved Visual Place Recognition Performance

Employing Universal Voting Schemes for Improved Visual Place Recognition Performance

URL: http://arxiv.org/abs/2405.02297v1
Date: Fri, 8 Mar 2024 14:19:14 GMT
Title: Employing Universal Voting Schemes for Improved Visual Place Recognition Performance
Authors: Maria Waheed, Michael Milford, Xiaojun Zhai, Maria Fasli, Klaus McDonald-Maier, Shoaib Ehsan,
Abstract summary: This paper analyses several voting schemes to maximise the place detection accuracy of a VPR ensemble set up. We take inspiration from a variety of voting schemes that are widely employed in fields such as politics and sociology.
Score: 16.242139178368923
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Visual Place Recognition has been the subject of many endeavours utilizing different ensemble approaches to improve VPR performance. Ideas like multi-process fusion, Fly-Inspired Voting Units, SwitchHit or Switch-Fuse involve combining different VPR techniques together, utilizing different strategies. However, a major aspect often common to many of these strategies is voting. Voting is an extremely relevant topic to explore in terms of its application and significance for any ensemble VPR setup. This paper analyses several voting schemes to maximise the place detection accuracy of a VPR ensemble set up and determine the optimal voting schemes for selection. We take inspiration from a variety of voting schemes that are widely employed in fields such as politics and sociology and it is evident via empirical data that the selection of the voting method influences the results drastically. The paper tests a wide variety of voting schemes to present the improvement in the VPR results for several data sets. We aim to determine whether a single optimal voting scheme exists or, much like in other fields of research, the selection of a voting technique is relative to its application and environment. We propose a ranking of these different voting methods from best to worst which allows for better selection. While presenting our results in terms of voting method's performance bounds, in form of radar charts, PR curves to showcase the difference in performance and a comparison methodology using a McNemar test variant to determine the statistical significance of the differences. This test is performed to further confirm the reliability of outcomes and draw comparisons for better and informed selection a voting technique.

Related papers

Representation Bias in Political Sample Simulations with Large Language Models [54.48283690603358]
This study seeks to identify and quantify biases in simulating political samples with Large Language Models. Using the GPT-3.5-Turbo model, we leverage data from the American National Election Studies, German Longitudinal Election Study, Zuobiao dataset, and China Family Panel Studies.
arXiv Detail & Related papers (2024-07-16T05:52:26Z)
Efficient Weighting Schemes for Auditing Instant-Runoff Voting Elections [57.67176250198289]
AWAIRE involves adaptively weighted averages of test statistics, essentially "learning" an effective set of hypotheses to test. We explore schemes and settings more extensively, to identify and recommend efficient choices for practice. A limitation of the current AWAIRE implementation is its restriction to a small number of candidates.
arXiv Detail & Related papers (2024-02-18T10:13:01Z)
Rank, Pack, or Approve: Voting Methods in Participatory Budgeting [2.326556516716391]
The Stanford Participatory Budgeting platform has been used to engage residents in more than 150 budgeting processes. We present a data set with anonymized budget opinions from these processes with K-approval, K-ranking or knapsack primary ballots. We use vote pairs with different voting methods to analyze the effect of voting methods on the cost of selected projects.
arXiv Detail & Related papers (2024-01-23T01:19:44Z)
Fair and Inclusive Participatory Budgeting: Voter Experience with Cumulative and Quadratic Voting Interfaces [1.4730691320093603]
Cumulative and quadratic voting are expressive, promoting fairness and inclusion. Despite these benefits, graphical voter interfaces for cumulative and quadratic voting are complex to implement and use effectively. This paper introduces an implementation and evaluation of cumulative and quadratic voting within a state-of-the-art voting platform: Stanford Participatory Budgeting.
arXiv Detail & Related papers (2023-08-08T15:45:55Z)
Adaptively Weighted Audits of Instant-Runoff Voting Elections: AWAIRE [61.872917066847855]
Methods for auditing instant-runoff voting (IRV) elections are either not risk-limiting or require cast vote records (CVRs), the voting system's electronic record of the votes on each ballot. We develop an RLA method that uses adaptively weighted averages of test supermartingales to efficiently audit IRV elections when CVRs are not available.
arXiv Detail & Related papers (2023-07-20T15:55:34Z)
Improving Recommendation System Serendipity Through Lexicase Selection [53.57498970940369]
We propose a new serendipity metric to measure the presence of echo chambers and homophily in recommendation systems. We then attempt to improve the diversity-preservation qualities of well known recommendation techniques by adopting a parent selection algorithm known as lexicase selection. Our results show that lexicase selection, or a mixture of lexicase selection and ranking, outperforms its purely ranked counterparts in terms of personalization, coverage and our specifically designed serendipity benchmark.
arXiv Detail & Related papers (2023-05-18T15:37:38Z)
An Evaluation and Ranking of Different Voting Schemes for Improved Visual Place Recognition [19.14779092252812]
We take inspiration from a variety of voting schemes that exist and are widely employed in other research fields such as politics and sociology. Some of these voting schemes include Condorcet voting, Broda Count and Plurality voting. We evaluate some of these voting techniques in a standardized testing of different VPR techniques, using a variety of VPR data sets.
arXiv Detail & Related papers (2023-05-09T18:24:33Z)
Data as voters: instance selection using approval-based multi-winner voting [1.597617022056624]
We present a novel approach to the instance selection problem in machine learning (or data mining) In our model, instances play a double role as voters and candidates. For SVMs, we have obtained slight increases in the average accuracy by using several voting rules that satisfy EJR or PJR.
arXiv Detail & Related papers (2023-04-19T22:00:23Z)
Private Multi-Winner Voting for Machine Learning [48.0093793427039]
We propose three new DP multi-winner mechanisms: Binary, $tau$, and Powerset voting. Binary voting operates independently per label through composition. $tau$ voting bounds votes optimally in their $ell$ norm for tight data-independent guarantees. Powerset voting operates over the entire binary vector by viewing the possible outcomes as a power set.
arXiv Detail & Related papers (2022-11-23T20:06:46Z)
Expected Frequency Matrices of Elections: Computation, Geometry, and Preference Learning [58.23459346724491]
We use the "map of elections" approach of Szufa et al. (AAMAS 2020) to analyze several well-known vote distributions. We draw the "skeleton map" of distributions, evaluate its robustness, and analyze its properties.
arXiv Detail & Related papers (2022-05-16T17:40:22Z)
Modeling Voters in Multi-Winner Approval Voting [24.002910959494923]
We study voting behavior in single-winner and multi-winner approval voting scenarios with varying degrees of uncertainty. We find that people generally manipulate their vote to obtain a better outcome, but often do not identify the optimal manipulation. We propose a novel model that takes into account the size of the winning set and human cognitive constraints.
arXiv Detail & Related papers (2020-12-04T19:24:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.