270 citations · 361 across the 6 of their papers we have counts for
7 papers
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…
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…
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…
Unsupervised Single Image Dehazing Using Dark Channel Prior Loss
Alona Golts, Daniel Freedman, Michael Elad
Single image dehazing is a critical stage in many modern-day autonomous vision applications. Early prior-based methods often involved a time-consuming minimization of a hand-crafte…
Deep-Energy: Unsupervised Training of Deep Neural Networks
Alona Golts, Daniel Freedman, Michael Elad
The success of deep learning has been due, in no small part, to the availability of large annotated datasets. Thus, a major bottleneck in current learning pipelines is the time-con…
Linearized Kernel Dictionary Learning
Alona Golts, Michael Elad
In this paper we present a new approach of incorporating kernels into dictionary learning. The kernel K-SVD algorithm (KKSVD), which has been introduced recently, shows an improvem…