xT: Nested Tokenization for Larger Context in Large Images (2403.01915v2)

Abstract: Modern computer vision pipelines handle large images in one of two sub-optimal ways: down-sampling or cropping. These two methods incur significant losses in the amount of information and context present in an image. There are many downstream applications in which global context matters as much as high frequency details, such as in real-world satellite imagery; in such cases researchers have to make the uncomfortable choice of which information to discard. We introduce xT, a simple framework for vision transformers which effectively aggregates global context with local details and can model large images end-to-end on contemporary GPUs. We select a set of benchmark datasets across classic vision tasks which accurately reflect a vision model's ability to understand truly large images and incorporate fine details over large scales and assess our method's improvement on them. xT is a streaming, two-stage architecture that adapts existing vision backbones and long sequence LLMs to effectively model large images without quadratic memory growth. We are able to increase accuracy by up to 8.6% on challenging classification tasks and $F_1$ score by 11.6 on context-dependent segmentation on images as large as 29,000 x 29,000 pixels.

• The paper introduces a nested tokenization strategy that enables vision transformers to capture both global context and fine details in large images, overcoming GPU memory constraints.
• The methodology employs a two-level tokenization process that segments images into regions and patches, using long-sequence models like Transformer-XL to enhance hierarchical feature encoding.
• Experimental results demonstrate up to an 8.6% improvement in classification accuracy and an 11.6-point increase in segmentation F1, highlighting its significance for high-resolution image tasks.

Nested Tokenization via xT Framework Enhances Contextual Understanding in Large Image Tasks

Introduction to xT

The xT framework introduces an innovative approach for enhancing large image processing within computer vision models, specifically targeting the constraints imposed by current methodologies that rely on down-sampling or cropping. This framework utilizes a nested tokenization strategy that effectively augments the ability of vision transformers to incorporate both global context and high-frequency local details within large images. By doing so, it addresses a critical limitation in contemporary computer vision pipelines, where the processing of large images is restricted by the memory capacities of available GPUs. The methodology ensures end-to-end modeling of large images without sacrificing detail or contextual awareness, marking an important evolution in handling real-world image datasets.

Methodology Overview

The core of the xT framework is its two-level nested tokenization process, which initially segments large images into manageable regions and further divides each region into patches for detailed analysis. This hierarchical analysis allows the model to process and understand both the macro and micro aspects of images effectively. The novelty lies in leveraging long-sequence models from the NLP domain, namely Transformer-XL and Mamba models, to process the features extracted from these nested tokens. Such an approach leads to significant improvements in both the theoretical and practical aspects of computer vision, navigating the limitations imposed by conventional GPUs.

• Nested Tokenization: The process starts with dividing a large image into smaller regions (R), each of which is further split into patches (P). This allows the model to manage high-resolution images by sequentially processing chunked regions.
• Hierarchical Encoding: Features extracted at the patch level undergo hierarchical encoding, which down-samples the features while retaining critical information. These encoded pieces then pass through context encoders, significantly expanding the model's comprehension over extensive contexts.
• Context Encoding with Long-Sequence Models: Utilizing Transformer-XL, the framework incorporates a natural long-sequence understanding from the NLP domain into the visual context, enabling recursive processing of previous sequence tokens via cross-attention mechanisms.

Experimental Results & Implications

Across several benchmarks tailored to evaluate performance on large images, the xT framework achieved notable accuracy improvements:

• Classification Tasks: Demonstrated up to 8.6% gain in accuracy.
• Segmentation Tasks: Showed an 11.6 increase in F1 score for context-dependent segmentation tasks.

These results underscore the framework's effectiveness in enhancing model performance for tasks requiring both detailed and global contextual understanding.

Future Developments and AI Implications

The introduction of xT points towards significant future developments in AI, particularly in seamlessly processing and understanding large-scale visual data without compromising on detail or context. This capacity is especially crucial for applications in satellite imagery analysis, medical imaging, and other fields dealing with high-resolution images.

Moreover, this framework opens up new possibilities in AI research by integrating techniques from NLP into visual processing, suggesting a more interdisciplinary approach in developing future models. The potential for xT to accommodate advancements in both hardware and algorithmic strategies further underscores its relevance and adaptability in the evolving landscape of AI technology.

Conclusion

By addressing the longstanding challenge of effectively processing large images within the constraints of current hardware, the xT framework represents a consequential step forward in computer vision. Its nested tokenization approach, combined with the use of long-sequence models, bridges the gap between local detail and global context understanding in large images. As the domain of AI continues to evolve, the principles and methodologies introduced by xT will likely inspire further research and innovation, pushing the boundaries of what's possible in computer vision and beyond.