10 citations · 18 across the 14 of their papers we have counts for
17 papers
DREAM: Deep Research Evaluation with Agentic Metrics
Elad Ben Avraham, Changhao Li, Ron Dorfman +8
Deep Research Agents generate analyst-grade reports, yet evaluating them remains challenging due to the absence of a single ground truth and the multidimensional nature of research…
DocVLM: Make Your VLM an Efficient Reader
Mor Shpigel Nacson, Aviad Aberdam, Roy Ganz +5
Vision-Language Models (VLMs) excel in diverse visual tasks but face challenges in document understanding, which requires fine-grained text processing. While typical visual tasks p…
TAP-VL: Text Layout-Aware Pre-training for Enriched Vision-Language Models
Jonathan Fhima, Elad Ben Avraham, Oren Nuriel +4
Vision-Language (VL) models have garnered considerable research interest; however, they still face challenges in effectively handling text within images. To address this limitation…
Question Aware Vision Transformer for Multimodal Reasoning
Roy Ganz, Yair Kittenplon, Aviad Aberdam +4
Vision-Language (VL) models have gained significant research focus, enabling remarkable advances in multimodal reasoning. These architectures typically comprise a vision encoder, a…
GRAM: Global Reasoning for Multi-Page VQA
Tsachi Blau, Sharon Fogel, Roi Ronen +6
The increasing use of transformer-based large language models brings forward the challenge of processing long sequences. In document visual question answering (DocVQA), leading met…
CLIPTER: Looking at the Bigger Picture in Scene Text Recognition
Aviad Aberdam, David Bensaïd, Alona Golts +5
Reading text in real-world scenarios often requires understanding the context surrounding it, especially when dealing with poor-quality text. However, current scene text recognizer…
Towards Models that Can See and Read
Roy Ganz, Oren Nuriel, Aviad Aberdam +3
Visual Question Answering (VQA) and Image Captioning (CAP), which are among the most popular vision-language tasks, have analogous scene-text versions that require reasoning from t…
Out-of-Vocabulary Challenge Report
Sergi Garcia-Bordils, Andrés Mafla, Ali Furkan Biten +5
This paper presents final results of the Out-Of-Vocabulary 2022 (OOV) challenge. The OOV contest introduces an important aspect that is not commonly studied by Optical Character Re…
Multimodal Semi-Supervised Learning for Text Recognition
Aviad Aberdam, Roy Ganz, Shai Mazor +1
Until recently, the number of public real-world text images was insufficient for training scene text recognizers. Therefore, most modern training methods rely on synthetic data and…
On Calibration of Scene-Text Recognition Models
Ron Slossberg, Oron Anschel, Amir Markovitz +6
In this work, we study the problem of word-level confidence calibration for scene-text recognition (STR). Although the topic of confidence calibration has been an active research a…
Sequence-to-Sequence Contrastive Learning for Text Recognition
Aviad Aberdam, Ron Litman, Shahar Tsiper +5
We propose a framework for sequence-to-sequence contrastive learning (SeqCLR) of visual representations, which we apply to text recognition. To account for the sequence-to-sequence…
When and How Can Deep Generative Models be Inverted?
Aviad Aberdam, Dror Simon, Michael Elad
Deep generative models (e.g. GANs and VAEs) have been developed quite extensively in recent years. Lately, there has been an increased interest in the inversion of such a model, i.…
Ada-LISTA: Learned Solvers Adaptive to Varying Models
Aviad Aberdam, Alona Golts, Michael Elad
Neural networks that are based on unfolding of an iterative solver, such as LISTA (learned iterative soft threshold algorithm), are widely used due to their accelerated performance…
Barycenters of Natural Images -- Constrained Wasserstein Barycenters for Image Morphing
Dror Simon, Aviad Aberdam
Image interpolation, or image morphing, refers to a visual transition between two (or more) input images. For such a transition to look visually appealing, its desirable properties…
On Multi-Layer Basis Pursuit, Efficient Algorithms and Convolutional Neural Networks
Jeremias Sulam, Aviad Aberdam, Amir Beck +1
Parsimonious representations are ubiquitous in modeling and processing information. Motivated by the recent Multi-Layer Convolutional Sparse Coding (ML-CSC) model, we herein genera…
Adversarial Noise Attacks of Deep Learning Architectures -- Stability Analysis via Sparse Modeled Signals
Yaniv Romano, Aviad Aberdam, Jeremias Sulam +1
Despite their impressive performance, deep convolutional neural networks (CNNs) have been shown to be sensitive to small adversarial perturbations. These nuisances, which one can b…
Multi-Layer Sparse Coding: The Holistic Way
Aviad Aberdam, Jeremias Sulam, Michael Elad
The recently proposed multi-layer sparse model has raised insightful connections between sparse representations and convolutional neural networks (CNN). In its original conception,…