Search Results for author: Leonid Karlinsky

Found 27 papers, 15 papers with code

Structured Video Tokens @ Ego4D PNR Temporal Localization Challenge 2022

no code implementations15 Jun 2022 Elad Ben-Avraham, Roei Herzig, Karttikeya Mangalam, Amir Bar, Anna Rohrbach, Leonid Karlinsky, Trevor Darrell, Amir Globerson

First, as both images and videos contain structured information, we enrich a transformer model with a set of \emph{object tokens} that can be used across images and videos.

Temporal Localization

Bringing Image Scene Structure to Video via Frame-Clip Consistency of Object Tokens

no code implementations13 Jun 2022 Elad Ben-Avraham, Roei Herzig, Karttikeya Mangalam, Amir Bar, Anna Rohrbach, Leonid Karlinsky, Trevor Darrell, Amir Globerson

We explore a particular instantiation of scene structure, namely a \emph{Hand-Object Graph}, consisting of hands and objects with their locations as nodes, and physical relations of contact/no-contact as edges.

Action Recognition Video Understanding

Unsupervised Domain Generalization by Learning a Bridge Across Domains

1 code implementation CVPR 2022 Sivan Harary, Eli Schwartz, Assaf Arbelle, Peter Staar, Shady Abu-Hussein, Elad Amrani, Roei Herzig, Amit Alfassy, Raja Giryes, Hilde Kuehne, Dina Katabi, Kate Saenko, Rogerio Feris, Leonid Karlinsky

The ability to generalize learned representations across significantly different visual domains, such as between real photos, clipart, paintings, and sketches, is a fundamental capacity of the human visual system.

Domain Generalization Self-Supervised Learning

CHARTER: heatmap-based multi-type chart data extraction

no code implementations28 Nov 2021 Joseph Shtok, Sivan Harary, Ophir Azulai, Adi Raz Goldfarb, Assaf Arbelle, Leonid Karlinsky

The digital conversion of information stored in documents is a great source of knowledge.

Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data

1 code implementation NeurIPS 2021 Ashraful Islam, Chun-Fu Chen, Rameswar Panda, Leonid Karlinsky, Rogerio Feris, Richard J. Radke

As the base dataset and unlabeled dataset are from different domains, projecting the target images in the class-domain of the base dataset with a fixed pretrained model might be sub-optimal.

cross-domain few-shot learning

Self-Supervised Classification Network

1 code implementation19 Mar 2021 Elad Amrani, Leonid Karlinsky, Alex Bronstein

To guarantee non-degenerate solutions (i. e., solutions where all labels are assigned to the same class) we propose a mathematically motivated variant of the cross-entropy loss that has a uniform prior asserted on the predicted labels.

Classification Contrastive Learning +3

A Maximal Correlation Approach to Imposing Fairness in Machine Learning

no code implementations30 Dec 2020 Joshua Lee, Yuheng Bu, Prasanna Sattigeri, Rameswar Panda, Gregory Wornell, Leonid Karlinsky, Rogerio Feris

As machine learning algorithms grow in popularity and diversify to many industries, ethical and legal concerns regarding their fairness have become increasingly relevant.

BIG-bench Machine Learning Fairness

AR-Net: Adaptive Frame Resolution for Efficient Action Recognition

1 code implementation ECCV 2020 Yue Meng, Chung-Ching Lin, Rameswar Panda, Prasanna Sattigeri, Leonid Karlinsky, Aude Oliva, Kate Saenko, Rogerio Feris

Specifically, given a video frame, a policy network is used to decide what input resolution should be used for processing by the action recognition model, with the goal of improving both accuracy and efficiency.

Action Recognition

TAFSSL: Task-Adaptive Feature Sub-Space Learning for few-shot classification

1 code implementation ECCV 2020 Moshe Lichtenstein, Prasanna Sattigeri, Rogerio Feris, Raja Giryes, Leonid Karlinsky

The field of Few-Shot Learning (FSL), or learning from very few (typically $1$ or $5$) examples per novel class (unseen during training), has received a lot of attention and significant performance advances in the recent literature.

Few-Shot Learning General Classification

A Broader Study of Cross-Domain Few-Shot Learning

2 code implementations ECCV 2020 Yunhui Guo, Noel C. Codella, Leonid Karlinsky, James V. Codella, John R. Smith, Kate Saenko, Tajana Rosing, Rogerio Feris

Extensive experiments on the proposed benchmark are performed to evaluate state-of-art meta-learning approaches, transfer learning approaches, and newer methods for cross-domain few-shot learning.

cross-domain few-shot learning Few-Shot Image Classification +1

MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification

no code implementations1 Dec 2019 Sivan Doveh, Eli Schwartz, Chao Xue, Rogerio Feris, Alex Bronstein, Raja Giryes, Leonid Karlinsky

In this work, we propose to employ tools inspired by the Differentiable Neural Architecture Search (D-NAS) literature in order to optimize the architecture for FSL without over-fitting.

Few-Shot Learning General Classification +1

Baby steps towards few-shot learning with multiple semantics

no code implementations5 Jun 2019 Eli Schwartz, Leonid Karlinsky, Rogerio Feris, Raja Giryes, Alex M. Bronstein

Learning from one or few visual examples is one of the key capabilities of humans since early infancy, but is still a significant challenge for modern AI systems.

Few-Shot Image Classification

RepMet: Representative-Based Metric Learning for Classification and Few-Shot Object Detection

1 code implementation CVPR 2019 Leonid Karlinsky, Joseph Shtok, Sivan Harary, Eli Schwartz, Amit Aides, Rogerio Feris, Raja Giryes, Alex M. Bronstein

Distance metric learning (DML) has been successfully applied to object classification, both in the standard regime of rich training data and in the few-shot scenario, where each category is represented by only a few examples.

Classification Few-Shot Object Detection +3

LaSO: Label-Set Operations networks for multi-label few-shot learning

2 code implementations CVPR 2019 Amit Alfassy, Leonid Karlinsky, Amit Aides, Joseph Shtok, Sivan Harary, Rogerio Feris, Raja Giryes, Alex M. Bronstein

We conduct numerous experiments showing promising results for the label-set manipulation capabilities of the proposed approach, both directly (using the classification and retrieval metrics), and in the context of performing data augmentation for multi-label few-shot learning.

Data Augmentation Few-Shot Learning +1

Co-regularized Alignment for Unsupervised Domain Adaptation

no code implementations NeurIPS 2018 Abhishek Kumar, Prasanna Sattigeri, Kahini Wadhawan, Leonid Karlinsky, Rogerio Feris, William T. Freeman, Gregory Wornell

Deep neural networks, trained with large amount of labeled data, can fail to generalize well when tested with examples from a \emph{target domain} whose distribution differs from the training data distribution, referred as the \emph{source domain}.

Unsupervised Domain Adaptation

RepMet: Representative-based metric learning for classification and one-shot object detection

1 code implementation12 Jun 2018 Leonid Karlinsky, Joseph Shtok, Sivan Harary, Eli Schwartz, Amit Aides, Rogerio Feris, Raja Giryes, Alex M. Bronstein

Distance metric learning (DML) has been successfully applied to object classification, both in the standard regime of rich training data and in the few-shot scenario, where each category is represented by only a few examples.

Classification Few-Shot Object Detection +4

Fine-Grained Recognition of Thousands of Object Categories With Single-Example Training

1 code implementation CVPR 2017 Leonid Karlinsky, Joseph Shtok, Yochay Tzur, Asaf Tzadok

We approach the problem of fast detection and recognition of a large number (thousands) of object categories while training on a very limited amount of examples, usually one per category.

Using body-anchored priors for identifying actions in single images

no code implementations NeurIPS 2010 Leonid Karlinsky, Michael Dinerstein, Shimon Ullman

The task is easy for humans but difficult for current approaches to object recognition, because action instances may be similar in terms of body pose, and often require detailed examination of relations between participating objects and body parts in order to be recognized.

Object Recognition

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