Register assisted aggregation for Visual Place Recognition
- URL: http://arxiv.org/abs/2405.11526v1
- Date: Sun, 19 May 2024 11:36:52 GMT
- Title: Register assisted aggregation for Visual Place Recognition
- Authors: Xuan Yu, Zhenyong Fu,
- Abstract summary: Visual Place Recognition (VPR) refers to the process of using computer vision to recognize the position of the current query image.
Previous methods often discarded useless features while uncontrolled discarding features that help improve recognition accuracy.
We propose a new feature aggregation method to address this issue. Specifically, in order to obtain global and local features that contain discriminative place information, we added some registers on top of the original image tokens.
- Score: 4.5476780843439535
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Visual Place Recognition (VPR) refers to the process of using computer vision to recognize the position of the current query image. Due to the significant changes in appearance caused by season, lighting, and time spans between query images and database images for retrieval, these differences increase the difficulty of place recognition. Previous methods often discarded useless features (such as sky, road, vehicles) while uncontrolled discarding features that help improve recognition accuracy (such as buildings, trees). To preserve these useful features, we propose a new feature aggregation method to address this issue. Specifically, in order to obtain global and local features that contain discriminative place information, we added some registers on top of the original image tokens to assist in model training. After reallocating attention weights, these registers were discarded. The experimental results show that these registers surprisingly separate unstable features from the original image representation and outperform state-of-the-art methods.
Related papers
- Breaking the Frame: Image Retrieval by Visual Overlap Prediction [53.17564423756082]
We propose a novel visual place recognition approach, VOP, that efficiently addresses occlusions and complex scenes.
The proposed method enables the identification of visible image sections without requiring expensive feature detection and matching.
arXiv Detail & Related papers (2024-06-23T20:00:20Z) - CricaVPR: Cross-image Correlation-aware Representation Learning for Visual Place Recognition [73.51329037954866]
We propose a robust global representation method with cross-image correlation awareness for visual place recognition.
Our method uses the attention mechanism to correlate multiple images within a batch.
Our method outperforms state-of-the-art methods by a large margin with significantly less training time.
arXiv Detail & Related papers (2024-02-29T15:05:11Z) - Regressing Transformers for Data-efficient Visual Place Recognition [10.156432076272475]
This work introduces a fresh perspective by framing place recognition as a regression problem.
By optimizing image descriptors to align directly with graded similarity labels, this approach enhances ranking capabilities without expensive re-ranking.
arXiv Detail & Related papers (2024-01-29T17:04:32Z) - PlaceFormer: Transformer-based Visual Place Recognition using Multi-Scale Patch Selection and Fusion [2.3020018305241337]
PlaceFormer is a transformer-based approach for visual place recognition.
PlaceFormer employs patch tokens from the transformer to create global image descriptors.
It selects patches that correspond to task-relevant areas in an image.
arXiv Detail & Related papers (2024-01-23T20:28:06Z) - Attribute Prototype Network for Any-Shot Learning [113.50220968583353]
We argue that an image representation with integrated attribute localization ability would be beneficial for any-shot, i.e. zero-shot and few-shot, image classification tasks.
We propose a novel representation learning framework that jointly learns global and local features using only class-level attributes.
arXiv Detail & Related papers (2022-04-04T02:25:40Z) - Robust Place Recognition using an Imaging Lidar [45.37172889338924]
We propose a methodology for robust, real-time place recognition using an imaging lidar.
Our method is truly-invariant and can tackle reverse revisiting and upside-down revisiting.
arXiv Detail & Related papers (2021-03-03T01:08:31Z) - Cross-Descriptor Visual Localization and Mapping [81.16435356103133]
Visual localization and mapping is the key technology underlying the majority of Mixed Reality and robotics systems.
We present three novel scenarios for localization and mapping which require the continuous update of feature representations.
Our data-driven approach is agnostic to the feature descriptor type, has low computational requirements, and scales linearly with the number of description algorithms.
arXiv Detail & Related papers (2020-12-02T18:19:51Z) - HM4: Hidden Markov Model with Memory Management for Visual Place
Recognition [54.051025148533554]
We develop a Hidden Markov Model approach for visual place recognition in autonomous driving.
Our algorithm, dubbed HM$4$, exploits temporal look-ahead to transfer promising candidate images between passive storage and active memory.
We show that this allows constant time and space inference for a fixed coverage area.
arXiv Detail & Related papers (2020-11-01T08:49:24Z) - City-Scale Visual Place Recognition with Deep Local Features Based on
Multi-Scale Ordered VLAD Pooling [5.274399407597545]
We present a fully-automated system for place recognition at a city-scale based on content-based image retrieval.
Firstly, we take a comprehensive analysis of visual place recognition and sketch out the unique challenges of the task.
Next, we propose yet a simple pooling approach on top of convolutional neural network activations to embed the spatial information into the image representation vector.
arXiv Detail & Related papers (2020-09-19T15:21:59Z) - Geometrically Mappable Image Features [85.81073893916414]
Vision-based localization of an agent in a map is an important problem in robotics and computer vision.
We propose a method that learns image features targeted for image-retrieval-based localization.
arXiv Detail & Related papers (2020-03-21T15:36:38Z)
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.