Official repository for the ICLR 2024 paper "Towards Seamless Adaptation of Pre-trained Models for Visual Place Recognition".
BoQ: A Place is Worth a Bag of learnable Queries (CVPR 2024)
Official repository for the CVPR 2024 paper "CricaVPR: Cross-image Correlation-aware Representation Learning for Visual Place Recognition".
Official repository for the AAAI 2024 paper "Deep Homography Estimation for Visual Place Recognition".
Official repository for the NeurIPS 2024 paper "SuperVLAD: Compact and Robust Image Descriptors for Visual Place Recognition".
Official repository for the AAAI 2025 paper "Focus on Local: Finding Reliable Discriminative Regions for Visual Place Recognition".
Official repository for the NeurIPS 2025 paper "Towards Implicit Aggregation: Robust Image Representation for Place Recognition in the Transformer Era".
This repository is containing an object classification & localization project for SINGLE object.
EFGHNet: A Versatile Image-to-Point Cloud Registration Network for Extreme Outdoor Environment
Matches image sequences given a similarity matrix
Official repository for the T-PAMI 2025 paper "SelaVPR++: Towards Seamless Adaptation of Foundation Models for Efficient Place Recognition".
dnn-locate is a python module for discriminative features localization based on neural networks.
Implementation of the paper "Style Transfer for Keypoint Matching under Adverse Condition"
EndoCheck is a web application that allows doctors to automatically process endoscopic images or videos of patients to screen for anomalies.
STM32 Controller+Image Localization NYCU
CampusVision AI predicts precise latitude/longitude, camera orientation, and region ID (1–15) for IIIT Hyderabad campus images. Features include a 15-region dataset with 6000 manually annotated images per color‐coded area, rich metadata (GPS, timestamp, orientation), and advanced CV algorithms for high‐accuracy geolocation.
Automatic localization of optic disc and fovea using only image processing techniques.
A set of markdown files containing summarization on selected surveys concerning medical image processing
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