22 citations · 50 across the 10 of their papers we have counts for
12 papers
Watch Where You Head: A View-biased Domain Gap in Gait Recognition and Unsupervised Adaptation
Gavriel Habib, Noa Barzilay, Or Shimshi +2
Gait Recognition is a computer vision task aiming to identify people by their walking patterns. Although existing methods often show high performance on specific datasets, they lac…
Norm-guided latent space exploration for text-to-image generation
Dvir Samuel, Rami Ben-Ari, Nir Darshan +2
Text-to-image diffusion models show great potential in synthesizing a large variety of concepts in new compositions and scenarios. However, the latent space of initial seeds is sti…
Chatting Makes Perfect: Chat-based Image Retrieval
Matan Levy, Rami Ben-Ari, Nir Darshan +1
Chats emerge as an effective user-friendly approach for information retrieval, and are successfully employed in many domains, such as customer service, healthcare, and finance. How…
Generating images of rare concepts using pre-trained diffusion models
Dvir Samuel, Rami Ben-Ari, Simon Raviv +2
Text-to-image diffusion models can synthesize high-quality images, but they have various limitations. Here we highlight a common failure mode of these models, namely, generating un…
Data Roaming and Quality Assessment for Composed Image Retrieval
Matan Levy, Rami Ben-Ari, Nir Darshan +1
The task of Composed Image Retrieval (CoIR) involves queries that combine image and text modalities, allowing users to express their intent more effectively. However, current CoIR…
Learnable Optimal Sequential Grouping for Video Scene Detection
Daniel Rotman, Yevgeny Yaroker, Elad Amrani +2
Video scene detection is the task of dividing videos into temporal semantic chapters. This is an important preliminary step before attempting to analyze heterogeneous video content…
Classification-Regression for Chart Comprehension
Matan Levy, Rami Ben-Ari, Dani Lischinski
Chart question answering (CQA) is a task used for assessing chart comprehension, which is fundamentally different from understanding natural images. CQA requires analyzing the rela…
TAEN: Temporal Aware Embedding Network for Few-Shot Action Recognition
Rami Ben-Ari, Mor Shpigel, Ophir Azulai +2
Classification of new class entities requires collecting and annotating hundreds or thousands of samples that is often prohibitively costly. Few-shot learning suggests learning to…
Noise Estimation Using Density Estimation for Self-Supervised Multimodal Learning
Elad Amrani, Rami Ben-Ari, Daniel Rotman +1
One of the key factors of enabling machine learning models to comprehend and solve real-world tasks is to leverage multimodal data. Unfortunately, annotation of multimodal data is…
Learning to Detect and Retrieve Objects from Unlabeled Videos
Elad Amrani, Rami Ben-Ari, Tal Hakim +1
Learning an object detector or retrieval requires a large data set with manual annotations. Such data sets are expensive and time consuming to create and therefore difficult to obt…
Classification and Detection in Mammograms with Weak Supervision via Dual Branch Deep Neural Net
Ran Bakalo, Rami Ben-Ari, Jacob Goldberger
The high cost of generating expert annotations, poses a strong limitation for supervised machine learning methods in medical imaging. Weakly supervised methods may provide a soluti…
Weakly and Semi Supervised Detection in Medical Imaging via Deep Dual Branch Net
Ran Bakalo, Jacob Goldberger, Rami Ben-Ari
This study presents a novel deep learning architecture for multi-class classification and localization of abnormalities in medical imaging illustrated through experiments on mammog…