most citedBuilding and better understanding vision-language models: insights and future directions

11 citations · 26 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV2025

FineVision: Open Data Is All You Need

Luis Wiedmann, Orr Zohar, Amir Mahla +6

The advancement of vision-language models (VLMs) is hampered by a fragmented landscape of inconsistent and contaminated public datasets. We introduce FineVision, a meticulously col…

cs.CL2025

DRISHTIKON: A Multimodal Multilingual Benchmark for Testing Language Models' Understanding on Indian Culture

Arijit Maji, Raghvendra Kumar, Akash Ghosh +6

We introduce DRISHTIKON, a first-of-its-kind multimodal and multilingual benchmark centered exclusively on Indian culture, designed to evaluate the cultural understanding of genera…

cs.LG2025★ 1 cited

SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics

Mustafa Shukor, Dana Aubakirova, Francesco Capuano +11

Vision-language models (VLMs) pretrained on large-scale multimodal datasets encode rich visual and linguistic knowledge, making them a strong foundation for robotics. Rather than t…

cs.AI2025★ 4 cited

SmolVLM: Redefining small and efficient multimodal models

Andrés Marafioti, Orr Zohar, Miquel Farré +14

Large Vision-Language Models (VLMs) deliver exceptional performance but require significant computational resources, limiting their deployment on mobile and edge devices. Smaller V…

cs.CV2025

SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion

Ahmed Nassar, Andres Marafioti, Matteo Omenetti +10

We introduce SmolDocling, an ultra-compact vision-language model targeting end-to-end document conversion. Our model comprehensively processes entire pages by generating DocTags, a…

cs.CL2025★ 10 cited

SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model

Loubna Ben Allal, Anton Lozhkov, Elie Bakouch +19

While large language models have facilitated breakthroughs in many applications of artificial intelligence, their inherent largeness makes them computationally expensive and challe…

cs.CV2024★ 11 cited

Building and better understanding vision-language models: insights and future directions

Hugo Laurençon, Andrés Marafioti, Victor Sanh +1

The field of vision-language models (VLMs), which take images and texts as inputs and output texts, is rapidly evolving and has yet to reach consensus on several key aspects of the…