AdaLAM: Revisiting Handcrafted Outlier Detection

• URL: http://arxiv.org/abs/2006.04250v1
• Date: Sun, 7 Jun 2020 20:16:36 GMT
• Title: AdaLAM: Revisiting Handcrafted Outlier Detection
• Authors: Luca Cavalli, Viktor Larsson, Martin Ralf Oswald, Torsten Sattler, Marc Pollefeys
• Abstract summary: Local feature matching is a critical component of many computer vision pipelines. We propose a hierarchical pipeline for effective outlier detection as well as integrate novel ideas which in sum lead to AdaLAM. AdaLAM is designed to effectively exploit modern parallel hardware, resulting in a very fast, yet very accurate, outlier filter.
• Score: 106.38441616109716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Local feature matching is a critical component of many computer vision pipelines, including among others Structure-from-Motion, SLAM, and Visual Localization. However, due to limitations in the descriptors, raw matches are often contaminated by a majority of outliers. As a result, outlier detection is a fundamental problem in computer vision, and a wide range of approaches have been proposed over the last decades. In this paper we revisit handcrafted approaches to outlier filtering. Based on best practices, we propose a hierarchical pipeline for effective outlier detection as well as integrate novel ideas which in sum lead to AdaLAM, an efficient and competitive approach to outlier rejection. AdaLAM is designed to effectively exploit modern parallel hardware, resulting in a very fast, yet very accurate, outlier filter. We validate AdaLAM on multiple large and diverse datasets, and we submit to the Image Matching Challenge (CVPR2020), obtaining competitive results with simple baseline descriptors. We show that AdaLAM is more than competitive to current state of the art, both in terms of efficiency and effectiveness.

Related papers

• Regularized Contrastive Partial Multi-view Outlier Detection [76.77036536484114]
  We propose a novel method named Regularized Contrastive Partial Multi-view Outlier Detection (RCPMOD) In this framework, we utilize contrastive learning to learn view-consistent information and distinguish outliers by the degree of consistency. Experimental results on four benchmark datasets demonstrate that our proposed approach could outperform state-of-the-art competitors.
  arXiv  Detail & Related papers  (2024-08-02T14:34:27Z)
• Efficient Generation of Hidden Outliers for Improved Outlier Detection [4.103281325880475]
  Outlier generation is a popular technique used for solving important outlier detection tasks. Existing methods disregard the'multiple views' property of outliers in high-dimensional spaces. We propose BISECT, a new outlier generation method that creates realistic outliers mimicking said property.
  arXiv  Detail & Related papers  (2024-02-06T09:48:33Z)
• Interactive Graph Convolutional Filtering [79.34979767405979]
  Interactive Recommender Systems (IRS) have been increasingly used in various domains, including personalized article recommendation, social media, and online advertising. These problems are exacerbated by the cold start problem and data sparsity problem. Existing Multi-Armed Bandit methods, despite their carefully designed exploration strategies, often struggle to provide satisfactory results in the early stages. Our proposed method extends interactive collaborative filtering into the graph model to enhance the performance of collaborative filtering between users and items.
  arXiv  Detail & Related papers  (2023-09-04T09:02:31Z)
• Are we really making much progress in unsupervised graph outlier detection? Revisiting the problem with new insight and superior method [36.72922385614812]
  UNOD focuses on detecting two kinds of typical outliers in graphs: the structural outlier and the contextual outlier. We find that the most widely-used outlier injection approach has a serious data leakage issue. We propose a new framework, Variance-based Graph Outlier Detection (VGOD), which combines our variance-based model and attribute reconstruction model to detect outliers in a balanced way.
  arXiv  Detail & Related papers  (2022-10-24T04:09:35Z)
• IPOF: An Extremely and Excitingly Simple Outlier Detection Booster via Infinite Propagation [30.91911545889579]
  Outlier detection is one of the most popular and continuously rising topics in the data mining field. In this paper, we consider the score-based outlier detection category and point out that the performance of current outlier detection algorithms might be further boosted by score propagation. Specifically, we propose Infinite propagation of Outlier Factor (iPOF) algorithm, an extremely and excitingly simple outlier detection booster via infinite propagation.
  arXiv  Detail & Related papers  (2021-08-01T03:48:09Z)
• Efficient Large Scale Inlier Voting for Geometric Vision Problems [3.1231364554255636]
  Outlier rejection and equivalently inlier set optimization is a key ingredient in numerous applications in computer vision. We present an efficient and general algorithm for outlier rejection based on "intersecting" $k$-dimensional surfaces in $Rd$. We demonstrate the versatility of the approach on several camera posing problems with a high number of matches at low inlier ratio.
  arXiv  Detail & Related papers  (2021-07-25T14:13:07Z)
• Anchor-free Small-scale Multispectral Pedestrian Detection [88.7497134369344]
  We propose a method for effective and efficient multispectral fusion of the two modalities in an adapted single-stage anchor-free base architecture. We aim at learning pedestrian representations based on object center and scale rather than direct bounding box predictions. Results show our method's effectiveness in detecting small-scaled pedestrians.
  arXiv  Detail & Related papers  (2020-08-19T13:13:01Z)
• Multi-scale Interactive Network for Salient Object Detection [91.43066633305662]
  We propose the aggregate interaction modules to integrate the features from adjacent levels. To obtain more efficient multi-scale features, the self-interaction modules are embedded in each decoder unit. Experimental results on five benchmark datasets demonstrate that the proposed method without any post-processing performs favorably against 23 state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-07-17T15:41:37Z)
• Equalization Loss for Long-Tailed Object Recognition [109.91045951333835]
  State-of-the-art object detection methods still perform poorly on large vocabulary and long-tailed datasets. We propose a simple but effective loss, named equalization loss, to tackle the problem of long-tailed rare categories. Our method achieves AP gains of 4.1% and 4.8% for the rare and common categories on the challenging LVIS benchmark.
  arXiv  Detail & Related papers  (2020-03-11T09:14:53Z)

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.