Context-Based Visual-Language Place Recognition
- URL: http://arxiv.org/abs/2410.19341v1
- Date: Fri, 25 Oct 2024 06:59:11 GMT
- Title: Context-Based Visual-Language Place Recognition
- Authors: Soojin Woo, Seong-Woo Kim,
- Abstract summary: A popular approach to vision-based place recognition relies on low-level visual features.
We introduce a novel VPR approach that remains robust to scene changes and does not require additional training.
Our method constructs semantic image descriptors by extracting pixel-level embeddings using a zero-shot, language-driven semantic segmentation model.
- Score: 4.737519767218666
- License:
- Abstract: In vision-based robot localization and SLAM, Visual Place Recognition (VPR) is essential. This paper addresses the problem of VPR, which involves accurately recognizing the location corresponding to a given query image. A popular approach to vision-based place recognition relies on low-level visual features. Despite significant progress in recent years, place recognition based on low-level visual features is challenging when there are changes in scene appearance. To address this, end-to-end training approaches have been proposed to overcome the limitations of hand-crafted features. However, these approaches still fail under drastic changes and require large amounts of labeled data to train models, presenting a significant limitation. Methods that leverage high-level semantic information, such as objects or categories, have been proposed to handle variations in appearance. In this paper, we introduce a novel VPR approach that remains robust to scene changes and does not require additional training. Our method constructs semantic image descriptors by extracting pixel-level embeddings using a zero-shot, language-driven semantic segmentation model. We validate our approach in challenging place recognition scenarios using real-world public dataset. The experiments demonstrate that our method outperforms non-learned image representation techniques and off-the-shelf convolutional neural network (CNN) descriptors. Our code is available at https: //github.com/woo-soojin/context-based-vlpr.
Related papers
- Locality Alignment Improves Vision-Language Models [55.275235524659905]
Vision language models (VLMs) have seen growing adoption in recent years, but many still struggle with basic spatial reasoning errors.
We propose a new efficient post-training stage for ViTs called locality alignment.
We show that locality-aligned backbones improve performance across a range of benchmarks.
arXiv Detail & Related papers (2024-10-14T21:01:01Z) - 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) - Deep Semantic-Visual Alignment for Zero-Shot Remote Sensing Image Scene
Classification [26.340737217001497]
Zero-shot learning (ZSL) allows for identifying novel classes that are not seen during training.
Previous ZSL models mainly depend on manually-labeled attributes or word embeddings extracted from language models to transfer knowledge from seen classes to novel classes.
We propose to collect visually detectable attributes automatically. We predict attributes for each class by depicting the semantic-visual similarity between attributes and images.
arXiv Detail & Related papers (2024-02-03T09:18:49Z) - Location-Aware Self-Supervised Transformers [74.76585889813207]
We propose to pretrain networks for semantic segmentation by predicting the relative location of image parts.
We control the difficulty of the task by masking a subset of the reference patch features visible to those of the query.
Our experiments show that this location-aware pretraining leads to representations that transfer competitively to several challenging semantic segmentation benchmarks.
arXiv Detail & Related papers (2022-12-05T16:24:29Z) - Scaling Up Visual and Vision-Language Representation Learning With Noisy
Text Supervision [57.031588264841]
We leverage a noisy dataset of over one billion image alt-text pairs, obtained without expensive filtering or post-processing steps.
A simple dual-encoder architecture learns to align visual and language representations of the image and text pairs using a contrastive loss.
We show that the scale of our corpus can make up for its noise and leads to state-of-the-art representations even with such a simple learning scheme.
arXiv Detail & Related papers (2021-02-11T10:08:12Z) - 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) - Region Comparison Network for Interpretable Few-shot Image
Classification [97.97902360117368]
Few-shot image classification has been proposed to effectively use only a limited number of labeled examples to train models for new classes.
We propose a metric learning based method named Region Comparison Network (RCN), which is able to reveal how few-shot learning works.
We also present a new way to generalize the interpretability from the level of tasks to categories.
arXiv Detail & Related papers (2020-09-08T07:29:05Z) - Complementing Representation Deficiency in Few-shot Image
Classification: A Meta-Learning Approach [27.350615059290348]
We propose a meta-learning approach with complemented representations network (MCRNet) for few-shot image classification.
In particular, we embed a latent space, where latent codes are reconstructed with extra representation information to complement the representation deficiency.
Our end-to-end framework achieves the state-of-the-art performance in image classification on three standard few-shot learning datasets.
arXiv Detail & Related papers (2020-07-21T13:25:54Z) - Distilling Localization for Self-Supervised Representation Learning [82.79808902674282]
Contrastive learning has revolutionized unsupervised representation learning.
Current contrastive models are ineffective at localizing the foreground object.
We propose a data-driven approach for learning in variance to backgrounds.
arXiv Detail & Related papers (2020-04-14T16:29:42Z)
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.