Img2Loc: Revisiting Image Geolocalization using Multi-modality Foundation Models and Image-based Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2403.19584v1
• Date: Thu, 28 Mar 2024 17:07:02 GMT
• Title: Img2Loc: Revisiting Image Geolocalization using Multi-modality Foundation Models and Image-based Retrieval-Augmented Generation
• Authors: Zhongliang Zhou, Jielu Zhang, Zihan Guan, Mengxuan Hu, Ni Lao, Lan Mu, Sheng Li, Gengchen Mai,
• Abstract summary: We present Img2Loc, a novel system that redefines image geolocalization as a text generation task. Img2Loc first employs CLIP-based representations to generate an image-based coordinate query database. It then uniquely combines query results with images itself, forming elaborate prompts customized for LMMs. When tested on benchmark datasets such as Im2GPS3k and YFCC4k, Img2Loc not only surpasses the performance of previous state-of-the-art models but does so without any model training.
• Score: 9.161203553842787
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Geolocating precise locations from images presents a challenging problem in computer vision and information retrieval.Traditional methods typically employ either classification, which dividing the Earth surface into grid cells and classifying images accordingly, or retrieval, which identifying locations by matching images with a database of image-location pairs. However, classification-based approaches are limited by the cell size and cannot yield precise predictions, while retrieval-based systems usually suffer from poor search quality and inadequate coverage of the global landscape at varied scale and aggregation levels. To overcome these drawbacks, we present Img2Loc, a novel system that redefines image geolocalization as a text generation task. This is achieved using cutting-edge large multi-modality models like GPT4V or LLaVA with retrieval augmented generation. Img2Loc first employs CLIP-based representations to generate an image-based coordinate query database. It then uniquely combines query results with images itself, forming elaborate prompts customized for LMMs. When tested on benchmark datasets such as Im2GPS3k and YFCC4k, Img2Loc not only surpasses the performance of previous state-of-the-art models but does so without any model training.

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)
• GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization [61.10806364001535]
  Worldwide Geo-localization aims to pinpoint the precise location of images taken anywhere on Earth. Existing approaches divide the globe into discrete geographic cells, transforming the problem into a classification task. We propose GeoCLIP, a novel CLIP-inspired Image-to-GPS retrieval approach that enforces alignment between the image and its corresponding GPS locations.
  arXiv  Detail & Related papers  (2023-09-27T20:54:56Z)
• GeoDiffusion: Text-Prompted Geometric Control for Object Detection Data Generation [91.01581867841894]
  We propose the GeoDiffusion, a simple framework that can flexibly translate various geometric conditions into text prompts. Our GeoDiffusion is able to encode not only the bounding boxes but also extra geometric conditions such as camera views in self-driving scenes.
  arXiv  Detail & Related papers  (2023-06-07T17:17:58Z)
• CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations [90.50864830038202]
  We present Contrastive Spatial Pre-Training (CSP), a self-supervised learning framework for geo-tagged images. We use a dual-encoder to separately encode the images and their corresponding geo-locations, and use contrastive objectives to learn effective location representations from images. CSP significantly boosts the model performance with 10-34% relative improvement with various labeled training data sampling ratios.
  arXiv  Detail & Related papers  (2023-05-01T23:11:18Z)
• Local and Global GANs with Semantic-Aware Upsampling for Image Generation [201.39323496042527]
  We consider generating images using local context. We propose a class-specific generative network using semantic maps as guidance. Lastly, we propose a novel semantic-aware upsampling method.
  arXiv  Detail & Related papers  (2022-02-28T19:24:25Z)
• Reconciliation of Statistical and Spatial Sparsity For Robust Image and Image-Set Classification [27.319334479994787]
  We propose a novel Joint Statistical and Spatial Sparse representation, dubbed textitJ3S, to model the image or image-set data for classification. We propose to solve the joint sparse coding problem based on the J3S model, by coupling the local and global image representations using joint sparsity. Experiments show that the proposed J3S-based image classification scheme outperforms the popular or state-of-the-art competing methods over FMD, UIUC, ETH-80 and YTC databases.
  arXiv  Detail & Related papers  (2021-06-01T06:33:24Z)
• Leveraging EfficientNet and Contrastive Learning for Accurate Global-scale Location Estimation [15.633461635276337]
  We propose a mixed classification-retrieval scheme for global-scale image geolocation. Our approach demonstrates very competitive performance on four public datasets.
  arXiv  Detail & Related papers  (2021-05-17T07:18:43Z)
• Hierarchical Attention Fusion for Geo-Localization [7.544917072241684]
  We introduce a hierarchical attention fusion network using multi-scale features for geo-localization. We extract the hierarchical feature maps from a convolutional neural network (CNN) and organically fuse the extracted features for image representations. Our training is self-supervised using adaptive weights to control the attention of feature emphasis from each hierarchical level.
  arXiv  Detail & Related papers  (2021-02-18T07:07:03Z)
• VIGOR: Cross-View Image Geo-localization beyond One-to-one Retrieval [19.239311087570318]
  Cross-view image geo-localization aims to determine the locations of street-view query images by matching with GPS-tagged reference images from aerial view. Recent works have achieved surprisingly high retrieval accuracy on city-scale datasets. We propose a new large-scale benchmark -- VIGOR -- for cross-View Image Geo-localization beyond One-to-one Retrieval.
  arXiv  Detail & Related papers  (2020-11-24T15:50:54Z)
• High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification [84.43394420267794]
  We propose a novel framework by learning high-order relation and topology information for discriminative features and robust alignment. Our framework significantly outperforms state-of-the-art by6.5%mAP scores on Occluded-Duke dataset.
  arXiv  Detail & Related papers  (2020-03-18T12:18:35Z)

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.