POINTS1.5: Building a Vision-Language Model towards Real World Applications (2412.08443v1)

Abstract: Vision-LLMs have made significant strides recently, demonstrating superior performance across a range of tasks, e.g. optical character recognition and complex diagram analysis. Building on this trend, we introduce a new vision-LLM, POINTS1.5, designed to excel in various real-world applications. POINTS1.5 is an enhancement of POINTS1.0 and incorporates several key innovations: i) We replace the original CLIP vision encoder, which had a fixed image resolution, with a NaViT-style vision encoder that supports native dynamic high resolution. This allows POINTS1.5 to process images of any resolution without needing to split them into tiles. ii) We add bilingual support to POINTS1.5, significantly enhancing its capability in Chinese. Due to the scarcity of open-source Chinese datasets for vision-LLMs, we collect numerous images from the Internet and annotate them using a combination of manual and automatic methods. iii) We propose a set of rigorous filtering methods for visual instruction tuning datasets. We comprehensively evaluate all these filtering methods, and choose the most effective ones to obtain the final visual instruction tuning set. Thanks to these innovations, POINTS1.5 significantly outperforms POINTS1.0 and demonstrates strong performance across a range of real-world applications. Notably, POINTS1.5-7B is trained on fewer than 4 billion tokens and ranks first on the OpenCompass leaderboard among models with fewer than 10 billion parameters

• The paper introduces a NaViT-style vision encoder that processes dynamic high-resolution images without tiling.
• It expands bilingual training by integrating a million high-quality Chinese samples with rigorous filtering.
• The model achieves top benchmarks on OCR, diagram analysis, and mathematical reasoning tasks with reduced hallucinations.

Overview of the POINTS1.5 Vision-LLM

The paper presents POINTS1.5, a vision-LLM (VLM) designed to advance real-world applications by enhancing the capabilities of its predecessor, POINTS1.0. Developed by researchers at WeChat AI, Tencent Inc., POINTS1.5 introduces several key innovations aimed at improving performance in diverse tasks, such as optical character recognition (OCR) and complex diagram analysis. The model employs a NaViT-style vision encoder, expands its bilingual training data, and incorporates rigorous dataset filtering methods, achieving robust outcomes across different benchmarks.

Innovations and Technical Advancements

POINTS1.5 introduces three primary innovations:

1. NaViT-Style Vision Encoder: Unlike its predecessor, which uses a CLIP-based vision encoder with fixed resolution capabilities, POINTS1.5 leverages a NaViT-style architecture that supports dynamic high-resolution image processing. This advancement eliminates the need to split images into tiles, preserving the spatial integrity of input images, a critical factor for tasks involving complex visual analysis.
2. Bilingual Support: This version addresses the limitation of predominantly English training data by significantly expanding the Chinese corpus for both the pre-training and visual instruction tuning stages. The researchers collected and annotated a million Chinese pre-training samples, balancing it with the existing English dataset. They applied CapFusion and perplexity filtering to assure high quality, allowing the model to perform well in Chinese-specific tasks, an area that remains underserved in open-source vision-LLMs.
3. Visual Instruction Tuning Set Filtering: Addressing the data quality issues observed in previous versions, POINTS1.5 implements a dual filtering strategy for instruction tuning datasets. This process involves leveraging a LLM to detect and rectify grammatical errors and ensuring questions cannot be answered without visual data. This approach refines dataset quality, enhancing the model's ability to adhere to visual instructions.

Evaluation and Performance

POINTS1.5 emerges as a top-ranking model on the OpenCompass leaderboard for models with fewer than 10 billion parameters, underscoring its ability to outperform larger models significantly. The model's robust evaluation spans several benchmarks, such as MathVista and HallusionBench, where it demonstrates exemplary mathematical reasoning and reduced hallucination tendencies compared to other vision-language frameworks.

Quantitatively, POINTS1.5 showcases superior scores in multiple open-source benchmarks, illustrating not just the utility of its architectural enhancements but also the valuable dataset curation efforts. The innovative integration of dynamic vision encoding and comprehensive dataset strategies distinguishes the model from its counterparts, contributing to a higher score over previous implementations like POINTS1.0.

Implications and Future Directions

The advancements made by POINTS1.5 highlight a pivotal shift in processing capabilities and data quality strategies necessary for vision-language interactions. The ability to handle high-resolution and multilingual datasets without compromising spatial context reflects a crucial advancement in model architecture and performance. This suggests a potential for real-world applications where precise visual comprehension and language support are crucial, such as automated translation in digital documents or intricate visual question-answering systems.

From a theoretical standpoint, POINTS1.5 serves as a template for future improvements in vision-LLMs by integrating state-of-the-art vision encoders with enriched datasets. The focus on maintaining quality across multilingual datasets also signifies a movement towards more inclusive AI systems capable of supporting diverse linguistic communities.

Looking ahead, building upon these innovations could involve exploring model expansion with larger datasets, including additional languages and image types, while focusing on optimizing training strategies to mitigate computational costs. Furthermore, the seamless integration of NaViT-style encoding within other modalities may set the stage for future multimodal AI systems that integrate even broader streams of data, such as video or 3D models, into language-based frameworks.

In conclusion, POINTS1.5 not only advances the technical capability of open-source vision-LLMs but also strategically sets the stage for future innovations in real-world AI applications and research.