1 citations · 2 across the 4 of their papers we have counts for
4 papers
Ill-Posed by Design: Probing Evidence Use in VLMs
Boaz Meivar, Shaked Perek, Shani Shvartzman +2
Counterfactual analysis is widely used to study evidence use in vision-language models, but its diagnostic value is limited on well-posed tasks: when several cues independently sup…
Balanced Thinking: Improving Chain of Thought Training in Vision Language Models
Shaked Perek, Ben Wiesel, Avihu Dekel +2
Multimodal reasoning in vision-language models (VLMs) typically relies on a two-stage process: supervised fine-tuning (SFT) and reinforcement learning (RL). In standard SFT, all to…
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…
Towards Multimodal In-Context Learning for Vision & Language Models
Sivan Doveh, Shaked Perek, M. Jehanzeb Mirza +5
State-of-the-art Vision-Language Models (VLMs) ground the vision and the language modality primarily via projecting the vision tokens from the encoder to language-like tokens, whic…