Search Results for author:
Found 28 papers, 10 papers with code
FaceCoresetNet: Differentiable Coresets for Face Set Recognition
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.
Coarse-to-Fine Multi-Scene Pose Regression with Transformers
Absolute camera pose regressors estimate the position and orientation of a camera given the captured image alone.
Image Segmentation via Probabilistic Graph Matching
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.
Age-Invariant Face Embedding using the Wasserstein Distance
In this work, we study face verification in datasets where images of the same individuals exhibit significant age differences.
Deep Convolutional Tables: Deep Learning without Convolutions
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.
HyperPose: Camera Pose Localization using Attention Hypernetworks
In this study, we propose the use of attention hypernetworks in camera pose localization.
Learning to Localize in Unseen Scenes with Relative Pose Regressors
In practice, however, the performance of RPRs is significantly degraded in unseen scenes.
Estimating Extreme 3D Image Rotation with Transformer Cross-Attention
In this work, we propose a cross-attention-based approach that utilizes CNN feature maps and a Transformer-Encoder, to compute the cross-attention between the activation maps of the image pairs, which is shown to be an improved equivalent of the 4D correlation volume, used in previous works.
Learning to embed semantic similarity for joint image-text retrieval
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.
Camera Pose Auto-Encoders for Improving Pose Regression
In this work, we introduce Camera Pose Auto-Encoders (PAEs), multilayer perceptrons that are trained via a Teacher-Student approach to encode camera poses using APRs as their teachers.
FSGANv2: Improved Subject Agnostic Face Swapping and Reenactment
Unlike previous work, we offer a subject agnostic swapping scheme that can be applied to pairs of faces without requiring training on those faces.
Synthesis and Reconstruction of Fingerprints using Generative Adversarial Networks
Deep learning-based models have been shown to improve the accuracy of fingerprint recognition.
Learning Multi-Scene Absolute Pose Regression with Transformers
Absolute camera pose regressors estimate the position and orientation of a camera from the captured image alone.
Paying Attention to Activation Maps in Camera Pose Regression
Transformers are applied to encode the sequential activation maps as latent vectors, used for camera pose regression.
Attention-Based Multimodal Image Matching
We propose an attention-based approach for multimodal image patch matching using a Transformer encoder attending to the feature maps of a multiscale Siamese CNN.
Ranked #1 on
Multimodal Patch Matching
on VisNir
Hierarchical Attention-based Age Estimation and Bias Estimation
We first introduce a dual image augmentation-aggregation approach based on attention.
Ranked #1 on
Age Estimation
on MORPH Album2
A Unified Approach to Kinship Verification
In this work, we propose a deep learning-based approach for kin verification using a unified multi-task learning scheme where all kinship classes are jointly learned.
DeepFake Detection Based on the Discrepancy Between the Face and its Context
Our approach involves two networks: (i) a face identification network that considers the face region bounded by a tight semantic segmentation, and (ii) a context recognition network that considers the face context (e. g., hair, ears, neck).
FSGAN: Subject Agnostic Face Swapping and Reenactment
We present Face Swapping GAN (FSGAN) for face swapping and reenactment.
Auto-ML Deep Learning for Rashi Scripts OCR
In this work we propose an OCR scheme for manuscripts printed in Rashi font that is an ancient Hebrew font and corresponding dialect used in religious Jewish literature, for more than 600 years.
Joint detection and matching of feature points in multimodal images
In this work, we propose a novel Convolutional Neural Network (CNN) architecture for the joint detection and matching of feature points in images acquired by different sensors using a single forward pass.
SelfKin: Self Adjusted Deep Model For Kinship Verification
In the area of homeland security (HLS) it is crucial to auto-detect if the person questioned is related to a wanted suspect, In the field of biometrics, kinship-verification can help to discriminate between families by photos and in the field of predicting or fashion it can help to predict an older or younger model of people faces.
Facial Landmarks Localization using Cascaded Neural Networks
The accurate localization of facial landmarks is at the core of face analysis tasks, such as face recognition and facial expression analysis, to name a few.
Multi-scale Processing of Noisy Images using Edge Preservation Losses
As our experiments show, we achieve high-quality results in the three aspects of faint edge detection, noisy image classification and natural image denoising.
Deep Multi-Spectral Registration Using Invariant Descriptor Learning
Our algorithm detects corners by Harris and matches them by a patch-metric learned on top of CIFAR-10 network descriptor.
Financial Time Series Prediction Using Deep Learning
A Deep Learning scheme is derived to predict the temporal trends of stocks and ETFs in NYSE or NASDAQ.
Signal Processing Statistical Finance
Registration and Fusion of Multi-Spectral Images Using a Novel Edge Descriptor
In this paper we introduce a fully end-to-end approach for multi-spectral image registration and fusion.
An algorithm for improving Non-Local Means operators via low-rank approximation
We present a method for improving a Non Local Means operator by computing its low-rank approximation.
