1 citations · 2 across the 5 of their papers we have counts for
7 papers
Spotlights: Discovering Improvement Opportunities in Software Repositories
Udi Barzelay, Ophir Azulai, Idan Friedman +9
Coding agents and evolutionary code-search systems can improve implementations once a target and evaluation criterion have been specified. Applying these methods to an existing sof…
VAREX: A Benchmark for Multi-Modal Structured Extraction from Documents
Udi Barzelay, Ophir Azulai, Inbar Shapira +4
We introduce VAREX (VARied-schema EXtraction), a benchmark for evaluating multimodal foundation models on structured data extraction from government forms. VAREX employs a Reverse…
Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence
Granite Vision Team, Leonid Karlinsky, Assaf Arbelle +60
We introduce Granite Vision, a lightweight large language model with vision capabilities, specifically designed to excel in enterprise use cases, particularly in visual document un…
KVP10k : A Comprehensive Dataset for Key-Value Pair Extraction in Business Documents
Oshri Naparstek, Roi Pony, Inbar Shapira +15
In recent years, the challenge of extracting information from business documents has emerged as a critical task, finding applications across numerous domains. This effort has attra…
BusiNet -- a Light and Fast Text Detection Network for Business Documents
Oshri Naparstek, Ophir Azulai, Daniel Rotman +3
For digitizing or indexing physical documents, Optical Character Recognition (OCR), the process of extracting textual information from scanned documents, is a vital technology. Whe…
CHARTER: heatmap-based multi-type chart data extraction
Joseph Shtok, Sivan Harary, Ophir Azulai +3
The digital conversion of information stored in documents is a great source of knowledge. In contrast to the documents text, the conversion of the embedded documents graphics, such…
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…