Results 1 to 10 of about 1,556,368 (169)
LightGlue: Local Feature Matching at Light Speed [PDF]
IEEE International Conference on Computer Vision, 2023 We introduce LightGlue, a deep neural network that learns to match local features across images. We revisit multiple design decisions of SuperGlue, the state of the art in sparse matching, and derive simple but effective improvements.
Philipp Lindenberger +2 more
semanticscholar +1 more source
LoFTR: Detector-Free Local Feature Matching with Transformers [PDF]
Computer Vision and Pattern Recognition, 2021 We present a novel method for local image feature matching. Instead of performing image feature detection, description, and matching sequentially, we propose to first establish pixel-wise dense matches at a coarse level and later refine the good matches ...
Jiaming Sun +4 more
semanticscholar +1 more source
Dataset Distillation by Matching Training Trajectories [PDF]
Computer Vision and Pattern Recognition, 2022 Dataset distillation is the task of synthesizing a small dataset such that a model trained on the synthetic set will match the test accuracy of the model trained on the full dataset.
George Cazenavette +4 more
semanticscholar +1 more source
Matching methods for causal inference: A review and a look forward. [PDF]
Statistical Science, 2010 When estimating causal effects using observational data, it is desirable to replicate a randomized experiment as closely as possible by obtaining treated and control groups with similar covariate distributions. This goal can often be achieved by choosing
E. Stuart
semanticscholar +1 more source
The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations [PDF]
, 2014 A bstractWe discuss the theoretical bases that underpin the automation of the computations of tree-level and next-to-leading order cross sections, of their matching to parton shower simulations, and of the merging of matched samples that differ by light ...
J. Alwall +10 more
semanticscholar +1 more source
SuperGlue: Learning Feature Matching With Graph Neural Networks [PDF]
Computer Vision and Pattern Recognition, 2019 This paper introduces SuperGlue, a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points.
Paul-Edouard Sarlin +3 more
semanticscholar +1 more source
Dataset Condensation with Distribution Matching [PDF]
IEEE Workshop/Winter Conference on Applications of Computer Vision, 2021 Computational cost of training state-of-the-art deep models in many learning problems is rapidly increasing due to more sophisticated models and larger datasets. A recent promising direction for reducing training cost is dataset condensation that aims to
Bo Zhao, Hakan Bilen
semanticscholar +1 more source
Moment Matching for Multi-Source Domain Adaptation [PDF]
IEEE International Conference on Computer Vision, 2018 Conventional unsupervised domain adaptation (UDA) assumes that training data are sampled from a single domain. This neglects the more practical scenario where training data are collected from multiple sources, requiring multi-source domain adaptation. We
Xingchao Peng +5 more
semanticscholar +1 more source
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
Computer Vision and Pattern Recognition, 2006 This paper presents a method for recognizing scene categories based on approximate global geometric correspondence. This technique works by partitioning the image into increasingly fine sub-regions and computing histograms of local features found inside ...
Svetlana Lazebnik, C. Schmid, J. Ponce
semanticscholar +1 more source
Pyramid Stereo Matching Network [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 Recent work has shown that depth estimation from a stereo pair of images can be formulated as a supervised learning task to be resolved with convolutional neural networks (CNNs).
Jia-Ren Chang, Yonghao Chen
semanticscholar +1 more source