Efficiency and Accuracy: NVILA – A New Family of Visual Language Models

Visual Language Models (VLMs) have made considerable progress in terms of accuracy in recent years. Their efficiency, on the other hand, has received significantly less attention. This article highlights NVILA, a family of open VLMs that aims to optimize both efficiency and accuracy.

The "Scale-then-Compress" Paradigm

NVILA builds upon the existing VILA model and improves its architecture through a "scale-then-compress" paradigm. First, the spatial and temporal resolutions are increased to extract more details from visual inputs, thereby increasing accuracy. Subsequently, the visual tokens are compressed to improve computational performance. This compression increases the information density of the visual tokens, preserving spatial and temporal details with fewer tokens. This approach allows NVILA to efficiently process high-resolution images and long videos.

Optimizing the Entire Lifecycle

In addition to architectural improvements, a systematic investigation was conducted to optimize NVILA's efficiency throughout its entire lifecycle – from training and fine-tuning to deployment. The results show a significant reduction in training costs, memory requirements during fine-tuning, as well as latency during pre-filling and decoding.

Efficiency Gains Without Compromising Accuracy

NVILA's efficiency gains do not come at the expense of accuracy. On the contrary, NVILA achieves or surpasses the accuracy of many leading open and proprietary VLMs in a variety of image and video benchmarks. This is achieved through the scaling of resolution and the subsequent compression of the visual tokens.

New Possibilities with NVILA

NVILA enables new application possibilities, including temporal localization, navigation in robotics, and medical imaging. These new capabilities open exciting perspectives for the application of VLMs in various fields.

Release of Code and Models

To ensure the reproducibility of the results and to promote further research in the field of efficient VLMs, the developers of NVILA will soon make the code and models publicly available.

Conclusion

NVILA represents a significant step in the development of efficient VLMs. By combining scaling and compression, as well as optimizing the entire lifecycle, NVILA offers both high accuracy and improved efficiency. The release of the code and models will drive further research and development in this promising area and open up new application possibilities.

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