Open-Set Semantic Uncertainty Aware Metric-Semantic Graph Matching

• URL: http://arxiv.org/abs/2409.11555v1
• Date: Tue, 17 Sep 2024 20:53:47 GMT
• Title: Open-Set Semantic Uncertainty Aware Metric-Semantic Graph Matching
• Authors: Kurran Singh, John J. Leonard,
• Abstract summary: A metric of semantic uncertainty for open-set object detections is calculated and incorporated into an object-level uncertainty tracking framework. The proposed methods are feasible for real-time use in marine environments for the robust, open-set, multi-object, semantic-uncertainty-aware loop closure detection.
• Score: 10.439907158831303
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Underwater object-level mapping requires incorporating visual foundation models to handle the uncommon and often previously unseen object classes encountered in marine scenarios. In this work, a metric of semantic uncertainty for open-set object detections produced by visual foundation models is calculated and then incorporated into an object-level uncertainty tracking framework. Object-level uncertainties and geometric relationships between objects are used to enable robust object-level loop closure detection for unknown object classes. The above loop closure detection problem is formulated as a graph-matching problem. While graph matching, in general, is NP-Complete, a solver for an equivalent formulation of the proposed graph matching problem as a graph editing problem is tested on multiple challenging underwater scenes. Results for this solver as well as three other solvers demonstrate that the proposed methods are feasible for real-time use in marine environments for the robust, open-set, multi-object, semantic-uncertainty-aware loop closure detection. Further experimental results on the KITTI dataset demonstrate that the method generalizes to large-scale terrestrial scenes.

Related papers

• SAM-LAD: Segment Anything Model Meets Zero-Shot Logic Anomaly Detection [17.32019706857109]
  Visual anomaly detection is vital in real-world applications, such as industrial defect detection and medical diagnosis. We propose SAM-LAD, a zero-shot, plug-and-play framework for logical anomaly detection in any scene. We validate our proposed SAM-LAD using various benchmarks, including industrial datasets.
  arXiv  Detail & Related papers  (2024-06-02T06:08:26Z)
• SemanticTopoLoop: Semantic Loop Closure With 3D Topological Graph Based on Quadric-Level Object Map [0.8158530638728501]
  Loop closure is one of the crucial components in SLAM. Traditional appearance-based methods, such as bag-of-words models, are often limited by local 2D features and the volume of training data.
  arXiv  Detail & Related papers  (2023-11-06T02:30:30Z)
• Weakly-supervised Contrastive Learning for Unsupervised Object Discovery [52.696041556640516]
  Unsupervised object discovery is promising due to its ability to discover objects in a generic manner. We design a semantic-guided self-supervised learning model to extract high-level semantic features from images. We introduce Principal Component Analysis (PCA) to localize object regions.
  arXiv  Detail & Related papers  (2023-07-07T04:03:48Z)
• Spatial-Temporal Graph Enhanced DETR Towards Multi-Frame 3D Object Detection [54.041049052843604]
  We present STEMD, a novel end-to-end framework that enhances the DETR-like paradigm for multi-frame 3D object detection. First, to model the inter-object spatial interaction and complex temporal dependencies, we introduce the spatial-temporal graph attention network. Finally, it poses a challenge for the network to distinguish between the positive query and other highly similar queries that are not the best match.
  arXiv  Detail & Related papers  (2023-07-01T13:53:14Z)
• Contrastive Object Detection Using Knowledge Graph Embeddings [72.17159795485915]
  We compare the error statistics of the class embeddings learned from a one-hot approach with semantically structured embeddings from natural language processing or knowledge graphs. We propose a knowledge-embedded design for keypoint-based and transformer-based object detection architectures.
  arXiv  Detail & Related papers  (2021-12-21T17:10:21Z)
• Probabilistic and Geometric Depth: Detecting Objects in Perspective [78.00922683083776]
  3D object detection is an important capability needed in various practical applications such as driver assistance systems. Monocular 3D detection, as an economical solution compared to conventional settings relying on binocular vision or LiDAR, has drawn increasing attention recently but still yields unsatisfactory results. This paper first presents a systematic study on this problem and observes that the current monocular 3D detection problem can be simplified as an instance depth estimation problem.
  arXiv  Detail & Related papers  (2021-07-29T16:30:33Z)
• Objects are Different: Flexible Monocular 3D Object Detection [87.82253067302561]
  We propose a flexible framework for monocular 3D object detection which explicitly decouples the truncated objects and adaptively combines multiple approaches for object depth estimation. Experiments demonstrate that our method outperforms the state-of-the-art method by relatively 27% for the moderate level and 30% for the hard level in the test set of KITTI benchmark.
  arXiv  Detail & Related papers  (2021-04-06T07:01:28Z)
• Video Anomaly Detection by Estimating Likelihood of Representations [21.879366166261228]
  Video anomaly is a challenging task because it involves solving many sub-tasks such as motion representation, object localization and action recognition. Traditionally, solutions to this task have focused on the mapping between video frames and their low-dimensional features, while ignoring the spatial connections of those features. Recent solutions focus on analyzing these spatial connections by using hard clustering techniques, such as K-Means, or applying neural networks to map latent features to a general understanding. In order to solve video anomaly in the latent feature space, we propose a deep probabilistic model to transfer this task into a density estimation problem.
  arXiv  Detail & Related papers  (2020-12-02T19:16:22Z)
• Slender Object Detection: Diagnoses and Improvements [74.40792217534]
  In this paper, we are concerned with the detection of a particular type of objects with extreme aspect ratios, namely textbfslender objects. For a classical object detection method, a drastic drop of $18.9%$ mAP on COCO is observed, if solely evaluated on slender objects.
  arXiv  Detail & Related papers  (2020-11-17T09:39:42Z)

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.