Zobrazeno 1 - 10
of 196
pro vyhledávání: '"Keller, Yosi"'
Autor:
Shapira, Gil, Keller, Yosi
In set-based face recognition, we aim to compute the most discriminative descriptor from an unbounded set of images and videos showing a single person. A discriminative descriptor balances two policies when aggregating information from a given set. T
Externí odkaz:
http://arxiv.org/abs/2308.14075
Absolute camera pose regressors estimate the position and orientation of a camera given the captured image alone. Typically, a convolutional backbone with a multi-layer perceptron (MLP) head is trained using images and pose labels to embed a single r
Externí odkaz:
http://arxiv.org/abs/2308.11783
Autor:
Heimowitz, Ayelet, Keller, Yosi
Publikováno v:
IEEE Transactions on Image Processing, vol. 25, no. 10, pp. 4743-4752, Oct. 2016
This work presents an unsupervised and semi-automatic image segmentation approach where we formulate the segmentation as a inference problem based on unary and pairwise assignment probabilities computed using low-level image cues. The inference is so
Externí odkaz:
http://arxiv.org/abs/2305.07954
Autor:
Dahan, Eran, Keller, Yosi
In this work, we study face verification in datasets where images of the same individuals exhibit significant age differences. This poses a major challenge for current face recognition and verification techniques. To address this issue, we propose a
Externí odkaz:
http://arxiv.org/abs/2305.02745
We propose a novel formulation of deep networks that do not use dot-product neurons and rely on a hierarchy of voting tables instead, denoted as Convolutional Tables (CT), to enable accelerated CPU-based inference. Convolutional layers are the most t
Externí odkaz:
http://arxiv.org/abs/2304.11706
Publikováno v:
CVPR 2024
The estimation of large and extreme image rotation plays a key role in multiple computer vision domains, where the rotated images are related by a limited or a non-overlapping field of view. Contemporary approaches apply convolutional neural networks
Externí odkaz:
http://arxiv.org/abs/2303.02615
Autor:
Ferens, Ron, Keller, Yosi
In this study, we propose the use of attention hypernetworks in camera pose localization. The dynamic nature of natural scenes, including changes in environment, perspective, and lighting, creates an inherent domain gap between the training and test
Externí odkaz:
http://arxiv.org/abs/2303.02610
Relative pose regressors (RPRs) localize a camera by estimating its relative translation and rotation to a pose-labelled reference. Unlike scene coordinate regression and absolute pose regression methods, which learn absolute scene parameters, RPRs c
Externí odkaz:
http://arxiv.org/abs/2303.02717
Autor:
Malali, Noam, Keller, Yosi
We present a deep learning approach for learning the joint semantic embeddings of images and captions in a Euclidean space, such that the semantic similarity is approximated by the L2 distances in the embedding space. For that, we introduce a metric
Externí odkaz:
http://arxiv.org/abs/2210.03838
Autor:
Shavit, Yoli, Keller, Yosi
Absolute pose regressor (APR) networks are trained to estimate the pose of the camera given a captured image. They compute latent image representations from which the camera position and orientation are regressed. APRs provide a different tradeoff be
Externí odkaz:
http://arxiv.org/abs/2207.05530