14 citations · 41 across the 23 of their papers we have counts for
23 papers
Adaptive Influence Graphs for Failure Attribution in Multi-Agent Systems
Yarden Bakish, Amir Dudai, Roy Ganz +4
Multi-agent LLM systems are increasingly deployed in real-world applications, where failures can be costly and difficult to localize. Despite growing efforts to automate failure at…
The Handoff Tax: Continuing Non-Native Trajectories in LLM Agents
Roy Ganz, Mor Shpigel Nacson, Adi Kalyanpur +1
Coding agents perform long-running tasks spanning dozens of model calls, tool uses, and code edits. As these runs unfold, users face a practical cost-quality trade-off: escalating…
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…
DODO: Discrete OCR Diffusion Models
Sean Man, Gilad Deutch, Roy Ganz +4
Optical Character Recognition (OCR) is a fundamental task for digitizing information, serving as a critical bridge between visual data and textual understanding. While modern Visio…
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…
Text-to-Image Generation Via Energy-Based CLIP
Roy Ganz, Michael Elad
Joint Energy Models (JEMs), while drawing significant research attention, have not been successfully scaled to real-world, high-resolution datasets. We present CLIP-JEM, a novel ap…
Adversaries With Incentives: A Strategic Alternative to Adversarial Robustness
Maayan Ehrenberg, Roy Ganz, Nir Rosenfeld
Adversarial training aims to defend against adversaries: malicious opponents whose sole aim is to harm predictive performance in any way possible. This presents a rather harsh pers…
Enhancing Consistency-Based Image Generation via Adversarialy-Trained Classification and Energy-Based Discrimination
Shelly Golan, Roy Ganz, Michael Elad
The recently introduced Consistency models pose an efficient alternative to diffusion algorithms, enabling rapid and good quality image synthesis. These methods overcome the slowne…
Paint by Inpaint: Learning to Add Image Objects by Removing Them First
Navve Wasserman, Noam Rotstein, Roy Ganz +1
Image editing has advanced significantly with the introduction of text-conditioned diffusion models. Despite this progress, seamlessly adding objects to images based on textual ins…
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…
CLIPAG: Towards Generator-Free Text-to-Image Generation
Roy Ganz, Michael Elad
Perceptually Aligned Gradients (PAG) refer to an intriguing property observed in robust image classification models, wherein their input gradients align with human perception and p…
FuseCap: Leveraging Large Language Models for Enriched Fused Image Captions
Noam Rotstein, David Bensaid, Shaked Brody +2
The advent of vision-language pre-training techniques enhanced substantial progress in the development of models for image captioning. However, these models frequently produce gene…
Class-Conditioned Transformation for Enhanced Robust Image Classification
Tsachi Blau, Roy Ganz, Chaim Baskin +2
Robust classification methods predominantly concentrate on algorithms that address a specific threat model, resulting in ineffective defenses against other threat models. Real-worl…
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…
Enhancing Diffusion-Based Image Synthesis with Robust Classifier Guidance
Bahjat Kawar, Roy Ganz, Michael Elad
Denoising diffusion probabilistic models (DDPMs) are a recent family of generative models that achieve state-of-the-art results. In order to obtain class-conditional generation, it…
Threat Model-Agnostic Adversarial Defense using Diffusion Models
Tsachi Blau, Roy Ganz, Bahjat Kawar +2
Deep Neural Networks (DNNs) are highly sensitive to imperceptible malicious perturbations, known as adversarial attacks. Following the discovery of this vulnerability in real-world…
Do Perceptually Aligned Gradients Imply Adversarial Robustness?
Roy Ganz, Bahjat Kawar, Michael Elad
Adversarially robust classifiers possess a trait that non-robust models do not -- Perceptually Aligned Gradients (PAG). Their gradients with respect to the input align well with hu…
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…
BIGRoC: Boosting Image Generation via a Robust Classifier
Roy Ganz, Michael Elad
The interest of the machine learning community in image synthesis has grown significantly in recent years, with the introduction of a wide range of deep generative models and means…
Improved Image Generation via Sparse Modeling
Roy Ganz, Michael Elad
The interest of the deep learning community in image synthesis has grown massively in recent years. Nowadays, deep generative methods, and especially Generative Adversarial Network…