activity
20232026
most citedVILA: On Pre-training for Visual Language Models

6 citations · 15 across the 14 of their papers we have counts for

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Showing 2024Show all

7 papers · 1 filter

cs.CV2024★ 1 cited

RADIOv2.5: Improved Baselines for Agglomerative Vision Foundation Models

Greg Heinrich, Mike Ranzinger, Hongxu +6

Agglomerative models have recently emerged as a powerful approach to training vision foundation models, leveraging multi-teacher distillation from existing models such as CLIP, DIN…

cs.CV2024★ 1 cited

NVILA: Efficient Frontier Visual Language Models

Zhijian Liu, Ligeng Zhu, Baifeng Shi +24

Visual language models (VLMs) have made significant advances in accuracy in recent years. However, their efficiency has received much less attention. This paper introduces NVILA, a…

cs.CV2024★ 1 cited

VILA-M3: Enhancing Vision-Language Models with Medical Expert Knowledge

Vishwesh Nath, Wenqi Li, Dong Yang +22

Generalist vision language models (VLMs) have made significant strides in computer vision, but they fall short in specialized fields like healthcare, where expert knowledge is esse…

cs.CV2024★ 1 cited

VILA-U: a Unified Foundation Model Integrating Visual Understanding and Generation

Yecheng Wu, Zhuoyang Zhang, Junyu Chen +9

VILA-U is a Unified foundation model that integrates Video, Image, Language understanding and generation. Traditional visual language models (VLMs) use separate modules for underst…

cs.CV2024★ 1 cited

LongVILA: Scaling Long-Context Visual Language Models for Long Videos

Yukang Chen, Fuzhao Xue, Dacheng Li +15

Long-context capability is critical for multi-modal foundation models, especially for long video understanding. We introduce LongVILA, a full-stack solution for long-context visual…

cs.CV2024

VILA: VILA Augmented VILA

Yunhao Fang, Ligeng Zhu, Yao Lu +7

While visual language model architectures and training infrastructures advance rapidly, data curation remains under-explored where quantity and quality become a bottleneck. Existin…

cs.CV2024★ 1 cited

X-VILA: Cross-Modality Alignment for Large Language Model

Hanrong Ye, De-An Huang, Yao Lu +8

We introduce X-VILA, an omni-modality model designed to extend the capabilities of large language models (LLMs) by incorporating image, video, and audio modalities. By aligning mod…