Adapting Fine-Grained Cross-View Localization to Areas without Fine Ground Truth

• URL: http://arxiv.org/abs/2406.00474v1
• Date: Sat, 1 Jun 2024 15:58:35 GMT
• Title: Adapting Fine-Grained Cross-View Localization to Areas without Fine Ground Truth
• Authors: Zimin Xia, Yujiao Shi, Hongdong Li, Julian F. P. Kooij,
• Abstract summary: This paper focuses on improving the performance of a trained model in a new target area by leveraging only the target-area images without fine GT. We propose a weakly supervised learning approach based on knowledge self-distillation. Our approach is validated using two recent state-of-the-art models on two benchmarks.
• Score: 56.565405280314884
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Given a ground-level query image and a geo-referenced aerial image that covers the query's local surroundings, fine-grained cross-view localization aims to estimate the location of the ground camera inside the aerial image. Recent works have focused on developing advanced networks trained with accurate ground truth (GT) locations of ground images. However, the trained models always suffer a performance drop when applied to images in a new target area that differs from training. In most deployment scenarios, acquiring fine GT, i.e. accurate GT locations, for target-area images to re-train the network can be expensive and sometimes infeasible. In contrast, collecting images with noisy GT with errors of tens of meters is often easy. Motivated by this, our paper focuses on improving the performance of a trained model in a new target area by leveraging only the target-area images without fine GT. We propose a weakly supervised learning approach based on knowledge self-distillation. This approach uses predictions from a pre-trained model as pseudo GT to supervise a copy of itself. Our approach includes a mode-based pseudo GT generation for reducing uncertainty in pseudo GT and an outlier filtering method to remove unreliable pseudo GT. Our approach is validated using two recent state-of-the-art models on two benchmarks. The results demonstrate that it consistently and considerably boosts the localization accuracy in the target area.

Related papers

• Towards Seamless Adaptation of Pre-trained Models for Visual Place Recognition [72.35438297011176]
  We propose a novel method to realize seamless adaptation of pre-trained models for visual place recognition (VPR) Specifically, to obtain both global and local features that focus on salient landmarks for discriminating places, we design a hybrid adaptation method. Experimental results show that our method outperforms the state-of-the-art methods with less training data and training time.
  arXiv  Detail & Related papers  (2024-02-22T12:55:01Z)
• Sample4Geo: Hard Negative Sampling For Cross-View Geo-Localisation [2.3020018305241337]
  We present a simplified but effective architecture based on contrastive learning with symmetric InfoNCE loss. Our framework consists of a narrow training pipeline that eliminates the need of using aggregation modules. Our work shows excellent performance on common cross-view datasets like CVUSA, CVACT, University-1652 and VIGOR.
  arXiv  Detail & Related papers  (2023-03-21T13:49:49Z)
• 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)
• Uncertainty-aware Vision-based Metric Cross-view Geolocalization [25.87104194833264]
  We present an end-to-end differentiable model that uses the ground and aerial images to predict a probability distribution over possible vehicle poses. We improve the previous state-of-the-art by a large margin even without ground or aerial data from the test region.
  arXiv  Detail & Related papers  (2022-11-22T10:23:20Z)
• City-wide Street-to-Satellite Image Geolocalization of a Mobile Ground Agent [38.140216125792755]
  Cross-view image geolocalization provides an estimate of an agent's global position by matching a local ground image to an overhead satellite image without the need for GPS. Our approach, called Wide-Area Geolocalization (WAG), combines a neural network with a particle filter to achieve global position estimates for agents moving in GPS-denied environments. WAG achieves position estimation accuracies on the order of 20 meters, a 98% reduction compared to a baseline training and weighting approach.
  arXiv  Detail & Related papers  (2022-03-10T19:54:12Z)
• Instance Localization for Self-supervised Detection Pretraining [68.24102560821623]
  We propose a new self-supervised pretext task, called instance localization. We show that integration of bounding boxes into pretraining promotes better task alignment and architecture alignment for transfer learning. Experimental results demonstrate that our approach yields state-of-the-art transfer learning results for object detection.
  arXiv  Detail & Related papers  (2021-02-16T17:58:57Z)
• PGL: Prior-Guided Local Self-supervised Learning for 3D Medical Image Segmentation [87.50205728818601]
  We propose a PriorGuided Local (PGL) self-supervised model that learns the region-wise local consistency in the latent feature space. Our PGL model learns the distinctive representations of local regions, and hence is able to retain structural information.
  arXiv  Detail & Related papers  (2020-11-25T11:03:11Z)
• Unsupervised Metric Relocalization Using Transform Consistency Loss [66.19479868638925]
  Training networks to perform metric relocalization traditionally requires accurate image correspondences. We propose a self-supervised solution, which exploits a key insight: localizing a query image within a map should yield the same absolute pose, regardless of the reference image used for registration. We evaluate our framework on synthetic and real-world data, showing our approach outperforms other supervised methods when a limited amount of ground-truth information is available.
  arXiv  Detail & Related papers  (2020-11-01T19:24:27Z)
• Anomaly Detection by One Class Latent Regularized Networks [36.67420338535258]
  Semi-supervised Generative Adversarial Networks (GAN)-based methods have been gaining popularity in anomaly detection task recently. A novel adversarial dual autoencoder network is proposed, in which the underlying structure of training data is captured in latent feature space. Experiments show that our model achieves the state-of-the-art results on MNIST and CIFAR10 datasets as well as GTSRB stop signs dataset.
  arXiv  Detail & Related papers  (2020-02-05T02:21:52Z)

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.