Search Results for author: Tal Hassner

Found 34 papers, 17 papers with code

TextStyleBrush: Transfer of Text Aesthetics from a Single Example

no code implementations15 Jun 2021 Praveen Krishnan, Rama Kovvuri, Guan Pang, Boris Vassilev, Tal Hassner

We present a novel approach for disentangling the content of a text image from all aspects of its appearance.

Scene Text

Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Images

1 code implementation15 Jun 2021 Vishal Asnani, Xi Yin, Tal Hassner, Xiaoming Liu

To tackle this problem, we propose a framework with two components: a Fingerprint Estimation Network (FEN), which estimates a GM fingerprint from a generated image by training with four constraints to encourage the fingerprint to have desired properties, and a Parsing Network (PN), which predicts network architecture and loss functions from the estimated fingerprints.

DeepFake Detection Face Swapping

TextOCR: Towards large-scale end-to-end reasoning for arbitrary-shaped scene text

no code implementations CVPR 2021 Amanpreet Singh, Guan Pang, Mandy Toh, Jing Huang, Wojciech Galuba, Tal Hassner

A crucial component for the scene text based reasoning required for TextVQA and TextCaps datasets involve detecting and recognizing text present in the images using an optical character recognition (OCR) system.

Optical Character Recognition Scene Text +1

A Multiplexed Network for End-to-End, Multilingual OCR

no code implementations CVPR 2021 Jing Huang, Guan Pang, Rama Kovvuri, Mandy Toh, Kevin J Liang, Praveen Krishnan, Xi Yin, Tal Hassner

Recent advances in OCR have shown that an end-to-end (E2E) training pipeline that includes both detection and recognition leads to the best results.

Optical Character Recognition

HyperSeg: Patch-wise Hypernetwork for Real-time Semantic Segmentation

1 code implementation CVPR 2021 Yuval Nirkin, Lior Wolf, Tal Hassner

We present a novel, real-time, semantic segmentation network in which the encoder both encodes and generates the parameters (weights) of the decoder.

Real-Time Semantic Segmentation

DeepFake Detection Based on the Discrepancy Between the Face and its Context

no code implementations27 Aug 2020 Yuval Nirkin, Lior Wolf, Yosi Keller, Tal Hassner

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).

DeepFake Detection Face Identification +2

Balancing Specialization, Generalization, and Compression for Detection and Tracking

no code implementations25 Sep 2019 Dotan Kaufman, Koby Bibas, Eran Borenstein, Michael Chertok, Tal Hassner

To this end, we propose a novel loss that balances compression and acceleration of a deep learning model vs. loss of generalization capabilities.

Model Compression

Transferability and Hardness of Supervised Classification Tasks

no code implementations ICCV 2019 Anh T. Tran, Cuong V. Nguyen, Tal Hassner

As a case study, we transfer a learned face recognition model to CelebA attribute classification tasks, showing state of the art accuracy for tasks estimated to be highly transferable.

Classification Face Recognition +1

Toward Understanding Catastrophic Forgetting in Continual Learning

no code implementations2 Aug 2019 Cuong V. Nguyen, Alessandro Achille, Michael Lam, Tal Hassner, Vijay Mahadevan, Stefano Soatto

As an application, we apply our procedure to study two properties of a task sequence: (1) total complexity and (2) sequential heterogeneity.

Continual Learning

ExpNet: Landmark-Free, Deep, 3D Facial Expressions

1 code implementation2 Feb 2018 Feng-Ju Chang, Anh Tuan Tran, Tal Hassner, Iacopo Masi, Ram Nevatia, Gerard Medioni

Our ExpNet CNN is applied directly to the intensities of a face image and regresses a 29D vector of 3D expression coefficients.

 Ranked #1 on 3D Facial Expression Recognition on 2017_test set (using extra training data)

3D Facial Expression Recognition Emotion Recognition +1

Extreme 3D Face Reconstruction: Seeing Through Occlusions

1 code implementation CVPR 2018 Anh Tuan Tran, Tal Hassner, Iacopo Masi, Eran Paz, Yuval Nirkin, Gerard Medioni

Motivated by the concept of bump mapping, we propose a layered approach which decouples estimation of a global shape from its mid-level details (e. g., wrinkles).

3D Face Reconstruction

FacePoseNet: Making a Case for Landmark-Free Face Alignment

3 code implementations24 Aug 2017 Feng-Ju Chang, Anh Tuan Tran, Tal Hassner, Iacopo Masi, Ram Nevatia, Gerard Medioni

Instead, we compare our FPN with existing methods by evaluating how they affect face recognition accuracy on the IJB-A and IJB-B benchmarks: using the same recognition pipeline, but varying the face alignment method.

 Ranked #1 on Facial Landmark Detection on 300W (Mean Error Rate metric)

3D Face Alignment Face Alignment +4

On Face Segmentation, Face Swapping, and Face Perception

1 code implementation22 Apr 2017 Yuval Nirkin, Iacopo Masi, Anh Tuan Tran, Tal Hassner, Gerard Medioni

To this end, we use the Labeled Faces in the Wild (LFW) benchmark and measure the effect of intra- and inter-subject face swapping on recognition.

Face Swapping

Temporal Tessellation: A Unified Approach for Video Analysis

1 code implementation ICCV 2017 Dotan Kaufman, Gil Levi, Tal Hassner, Lior Wolf

A test video is processed by forming correspondences between its clips and the clips of reference videos with known semantics, following which, reference semantics can be transferred to the test video.

Action Detection Video Captioning +2

The CUDA LATCH Binary Descriptor: Because Sometimes Faster Means Better

1 code implementation13 Sep 2016 Christopher Parker, Matthew Daiter, Kareem Omar, Gil Levi, Tal Hassner

Our results show that CLATCH provides high quality 3D reconstructions at fractions of the time required by other representations, with little, if any, loss of reconstruction quality.

Structure from Motion

Face Recognition Using Deep Multi-Pose Representations

no code implementations23 Mar 2016 Wael Abd-Almageed, Yue Wua, Stephen Rawlsa, Shai Harel, Tal Hassner, Iacopo Masi, Jongmoo Choi, Jatuporn Toy Leksut, Jungyeon Kim, Prem Natarajan, Ram Nevatia, Gerard Medioni

In our representation, a face image is processed by several pose-specific deep convolutional neural network (CNN) models to generate multiple pose-specific features.

Face Recognition Face Verification +1

GPU-Based Computation of 2D Least Median of Squares with Applications to Fast and Robust Line Detection

1 code implementation5 Oct 2015 Gil Shapira, Tal Hassner

The 2D Least Median of Squares (LMS) is a popular tool in robust regression because of its high breakdown point: up to half of the input data can be contaminated with outliers without affecting the accuracy of the LMS estimator.

Wide baseline stereo matching with convex bounded-distortion constraints

no code implementations10 Jun 2015 Meirav Galun, Tal Amir, Tal Hassner, Ronen Basri, Yaron Lipman

This paper focuses on the challenging problem of finding correspondences once approximate epipolar constraints are given.

Stereo Matching Stereo Matching Hand

LATCH: Learned Arrangements of Three Patch Codes

1 code implementation15 Jan 2015 Gil Levi, Tal Hassner

In order to provide more robustness, we instead propose a novel means of comparing pixel patches.

Effective Face Frontalization in Unconstrained Images

no code implementations CVPR 2015 Tal Hassner, Shai Harel, Eran Paz, Roee Enbar

"Frontalization" is the process of synthesizing frontal facing views of faces appearing in single unconstrained photos.

Face Recognition

Dense Correspondences Across Scenes and Scales

no code implementations24 Jun 2014 Moria Tau, Tal Hassner

Doing so allows scale invariant descriptors to be extracted anywhere in the image, not just in detected interest points.

Single View Depth Estimation from Examples

no code implementations14 Apr 2013 Tal Hassner, Ronen Basri

The known depths of the selected database objects act as shape priors which constrain the process of estimating the object's depth.

Depth Estimation

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