Search Results for author: Sagie Benaim

Found 24 papers, 16 papers with code

On Disentangled and Locally Fair Representations

no code implementations5 May 2022 Yaron Gurovich, Sagie Benaim, Lior Wolf

This problem is tackled through the lens of disentangled and locally fair representations.


Locally Shifted Attention With Early Global Integration

1 code implementation9 Dec 2021 Shelly Sheynin, Sagie Benaim, Adam Polyak, Lior Wolf

The separation of the attention layer into local and global counterparts allows for a low computational cost in the number of patches, while still supporting data-dependent localization already at the first layer, as opposed to the static positioning in other visual transformers.

Image Classification

Text2Mesh: Text-Driven Neural Stylization for Meshes

1 code implementation6 Dec 2021 Oscar Michel, Roi Bar-On, Richard Liu, Sagie Benaim, Rana Hanocka

In order to modify style, we obtain a similarity score between a text prompt (describing style) and a stylized mesh by harnessing the representational power of CLIP.

Neural Stylization

Image-Based CLIP-Guided Essence Transfer

1 code implementation24 Oct 2021 Hila Chefer, Sagie Benaim, Roni Paiss, Lior Wolf

We make the distinction between (i) style transfer, in which a source image is manipulated to match the textures and colors of a target image, and (ii) essence transfer, in which one edits the source image to include high-level semantic attributes from the target.

Domain Adaptation Style Transfer

Local-Global Shifting Vision Transformers

no code implementations29 Sep 2021 Shelly Sheynin, Sagie Benaim, Adam Polyak, Lior Wolf

Due to the expensive quadratic cost of the attention mechanism, either a large patch size is used, resulting in coarse-grained global interactions, or alternatively, attention is applied only on a local region of the image at the expense of long-range interactions.

Image Classification

JOKR: Joint Keypoint Representation for Unsupervised Cross-Domain Motion Retargeting

1 code implementation17 Jun 2021 Ron Mokady, Rotem Tzaban, Sagie Benaim, Amit H. Bermano, Daniel Cohen-Or

To alleviate this problem, we introduce JOKR - a JOint Keypoint Representation that captures the motion common to both the source and target videos, without requiring any object prior or data collection.

Disentanglement motion retargeting

Identity and Attribute Preserving Thumbnail Upscaling

no code implementations30 May 2021 Noam Gat, Sagie Benaim, Lior Wolf

We consider the task of upscaling a low resolution thumbnail image of a person, to a higher resolution image, which preserves the person's identity and other attributes.

A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detection

2 code implementations ICCV 2021 Shelly Sheynin, Sagie Benaim, Lior Wolf

We demonstrate the superiority of our method on both the one-shot and few-shot settings, on the datasets of Paris, CIFAR10, MNIST and FashionMNIST as well as in the setting of defect detection on MVTec.

Defect Detection Few Shot Anomaly Detection

Masked Based Unsupervised Content Transfer

1 code implementation ICLR 2020 Ron Mokady, Sagie Benaim, Lior Wolf, Amit Bermano

We consider the problem of translating, in an unsupervised manner, between two domains where one contains some additional information compared to the other.

Translation Weakly-Supervised Semantic Segmentation

Evaluation Metrics for Conditional Image Generation

no code implementations26 Apr 2020 Yaniv Benny, Tomer Galanti, Sagie Benaim, Lior Wolf

We present two new metrics for evaluating generative models in the class-conditional image generation setting.

Conditional Image Generation

Structural-analogy from a Single Image Pair

1 code implementation5 Apr 2020 Sagie Benaim, Ron Mokady, Amit Bermano, Daniel Cohen-Or, Lior Wolf

In this paper, we explore the capabilities of neural networks to understand image structure given only a single pair of images, A and B.

Translation Unsupervised Image-To-Image Translation

Unsupervised Learning of the Set of Local Maxima

no code implementations ICLR 2019 Lior Wolf, Sagie Benaim, Tomer Galanti

Two functions are learned: (i) a set indicator c, which is a binary classifier, and (ii) a comparator function h that given two nearby samples, predicts which sample has the higher value of the unknown function v. Loss terms are used to ensure that all training samples x are a local maxima of v, according to h and satisfy c(x)=1.

Anomaly Detection General Classification

Emerging Disentanglement in Auto-Encoder Based Unsupervised Image Content Transfer

2 code implementations ICLR 2019 Ori Press, Tomer Galanti, Sagie Benaim, Lior Wolf

Thus, in the above example, we can create, for every person without glasses a version with the glasses observed in any face image.


Domain Intersection and Domain Difference

1 code implementation ICCV 2019 Sagie Benaim, Michael Khaitov, Tomer Galanti, Lior Wolf

We present a method for recovering the shared content between two visual domains as well as the content that is unique to each domain.

Mask Based Unsupervised Content Transfer

1 code implementation15 Jun 2019 Ron Mokady, Sagie Benaim, Lior Wolf, Amit Bermano

We consider the problem of translating, in an unsupervised manner, between two domains where one contains some additional information compared to the other.

Translation Weakly-Supervised Semantic Segmentation

Semi-Supervised Monaural Singing Voice Separation With a Masking Network Trained on Synthetic Mixtures

1 code implementation14 Dec 2018 Michael Michelashvili, Sagie Benaim, Lior Wolf

We study the problem of semi-supervised singing voice separation, in which the training data contains a set of samples of mixed music (singing and instrumental) and an unmatched set of instrumental music.

Music Source Separation Speech Separation

Risk Bounds for Unsupervised Cross-Domain Mapping with IPMs

no code implementations23 Jul 2018 Tomer Galanti, Sagie Benaim, Lior Wolf

The recent empirical success of unsupervised cross-domain mapping algorithms, between two domains that share common characteristics, is not well-supported by theoretical justifications.

One-Shot Unsupervised Cross Domain Translation

2 code implementations NeurIPS 2018 Sagie Benaim, Lior Wolf

Given a single image x from domain A and a set of images from domain B, our task is to generate the analogous of x in B.

Translation Unsupervised Image-To-Image Translation +1

Estimating the Success of Unsupervised Image to Image Translation

1 code implementation ECCV 2018 Sagie Benaim, Tomer Galanti, Lior Wolf

While in supervised learning, the validation error is an unbiased estimator of the generalization (test) error and complexity-based generalization bounds are abundant, no such bounds exist for learning a mapping in an unsupervised way.

Generalization Bounds Translation +1

The Role of Minimal Complexity Functions in Unsupervised Learning of Semantic Mappings

no code implementations ICLR 2018 Tomer Galanti, Lior Wolf, Sagie Benaim

We discuss the feasibility of the following learning problem: given unmatched samples from two domains and nothing else, learn a mapping between the two, which preserves semantics.

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