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06/08/2023
SNAP: Self-Supervised Neural Maps for Visual Positioning and Semantic Understanding
Semantic 2D maps are commonly used by humans and machines for navigation...
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06/16/2022
TUSK: Task-Agnostic Unsupervised Keypoints
Existing unsupervised methods for keypoint learning rely heavily on the ...
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07/25/2021
Efficient Large Scale Inlier Voting for Geometric Vision Problems
Outlier rejection and equivalently inlier set optimization is a key ingr...
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03/25/2021
COTR: Correspondence Transformer for Matching Across Images
We propose a novel framework for finding correspondences in images based...
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05/08/2017
Learning non-maximum suppression
Object detectors have hugely profited from moving towards an end-to-end ...
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03/24/2016
Simple Does It: Weakly Supervised Instance and Semantic Segmentation
Semantic labelling and instance segmentation are two tasks that require ...
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02/03/2016
How Far are We from Solving Pedestrian Detection?
Encouraged by the recent progress in pedestrian detection, we investigat...
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02/17/2015
What makes for effective detection proposals?
Current top performing object detectors employ detection proposals to gu...
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01/23/2015
Taking a Deeper Look at Pedestrians
In this paper we study the use of convolutional neural networks (convnet...
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11/16/2014
Ten Years of Pedestrian Detection, What Have We Learned?
Paper-by-paper results make it easy to miss the forest for the trees.We ...
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06/26/2014