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20172026
most citedLearning non-maximum suppression

36 citations · 36 across the 4 of their papers we have counts for

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6 papers · 1 filter

cs.CV2026

UNIGEOCLIP: Unified Geospatial Contrastive Learning

Guillaume Astruc, Eduard Trulls, Jan Hosang +2

The growing availability of co-located geospatial data spanning aerial imagery, street-level views, elevation models, text, and geographic coordinates offers a unique opportunity f…

cs.CV2025

Scaling Image Geo-Localization to Continent Level

Philipp Lindenberger, Paul-Edouard Sarlin, Jan Hosang +4

Determining the precise geographic location of an image at a global scale remains an unsolved challenge. Standard image retrieval techniques are inefficient due to the sheer volume…

cs.CV2023

SNAP: Self-Supervised Neural Maps for Visual Positioning and Semantic Understanding

Paul-Edouard Sarlin, Eduard Trulls, Marc Pollefeys +2

Semantic 2D maps are commonly used by humans and machines for navigation purposes, whether it's walking or driving. However, these maps have limitations: they lack detail, often co…

cs.CV2021

Efficient Large Scale Inlier Voting for Geometric Vision Problems

Dror Aiger, Simon Lynen, Jan Hosang +1

Outlier rejection and equivalently inlier set optimization is a key ingredient in numerous applications in computer vision such as filtering point-matches in camera pose estimation…

cs.CV2021

COTR: Correspondence Transformer for Matching Across Images

Wei Jiang, Eduard Trulls, Jan Hosang +2

We propose a novel framework for finding correspondences in images based on a deep neural network that, given two images and a query point in one of them, finds its correspondence…

cs.CV201736 cited

Learning non-maximum suppression

Jan Hosang, Rodrigo Benenson, Bernt Schiele

Object detectors have hugely profited from moving towards an end-to-end learning paradigm: proposals, features, and the classifier becoming one neural network improved results two-…