ConvLLaVA: Hierarchical Backbones as Visual Encoder for Large Multimodal Models
(2405.15738)Abstract
High-resolution Large Multimodal Models (LMMs) encounter the challenges of excessive visual tokens and quadratic visual complexity. Current high-resolution LMMs address the quadratic complexity while still generating excessive visual tokens. However, the redundancy in visual tokens is the key problem as it leads to more substantial compute. To mitigate this issue, we propose ConvLLaVA, which employs ConvNeXt, a hierarchical backbone, as the visual encoder of LMM to replace Vision Transformer (ViT). ConvLLaVA compresses high-resolution images into information-rich visual features, effectively preventing the generation of excessive visual tokens. To enhance the capabilities of ConvLLaVA, we propose two critical optimizations. Since the low-resolution pretrained ConvNeXt underperforms when directly applied on high resolution, we update it to bridge the gap. Moreover, since ConvNeXt's original compression ratio is inadequate for much higher resolution inputs, we train a successive stage to further compress the visual tokens, thereby reducing redundancy. These optimizations enable ConvLLaVA to support inputs of 1536x1536 resolution generating only 576 visual tokens, capable of handling images of arbitrary aspect ratios. Experimental results demonstrate that our method achieves competitive performance with state-of-the-art models on mainstream benchmarks. The ConvLLaVA model series are publicly available at https://github.com/alibaba/conv-llava.
Overview
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ConvLLaVA is a novel approach that addresses the challenges of excessive visual tokens and computational complexity in high-resolution Large Multimodal Models (LMMs) by using ConvNeXt as a hierarchical backbone instead of the Vision Transformer (ViT).
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The research includes key optimizations for high-resolution inputs, such as updating ConvNeXt for better performance and adding a fifth compression stage, making the model competitive with state-of-the-art benchmarks and more efficient in processing high-resolution images.
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Theoretical and practical implications of ConvLLaVA emphasize its efficiency in reducing computational load while maintaining high performance, making it suitable for applications requiring detailed visual understanding like medical imaging and autonomous driving.
ConvLLaVA: Compressing High-Resolution Visual Information for Efficient Multimodal Models
The paper introduces ConvLLaVA, a new approach to address the challenges encountered by high-resolution Large Multimodal Models (LMMs). The primary motivation underlying this research is to tackle the issues of excessive visual tokens and computational complexity, both of which significantly hinder the performance and efficiency of LMMs, especially when dealing with high-resolution images.
Key Contributions
Hierarchical Backbone as Visual Encoder: The authors propose using ConvNeXt, a hierarchical backbone, as the visual encoder instead of the commonly used Vision Transformer (ViT). Hierarchical backbones inherently compress information across stages, unlike ViT, which results in excessive visual token generation. This hierarchical structure effectively alleviates the computational burden on the Large Language Model (LLM) by reducing the number of visual tokens.
Optimizations for High-Resolution Inputs: The research identifies and addresses two significant optimization challenges:
- Updating ConvNeXt for High-Resolution: ConvNeXt pretrained on low resolutions underperforms when applied directly to high-resolution images. By updating ConvNeXt, the model's general capability improves, making it comparable to ViT on general benchmarks and superior on fine-grained benchmarks.
- Training Successive Compression Stage: To handle images with higher resolutions, a fifth stage is added to further compress visual information, reducing redundancy and enabling the model to manage high-resolution inputs without generating an excessive number of visual tokens.
Experimental Results
The experimental results highlight ConvLLaVA's competitive performance relative to state-of-the-art models on mainstream benchmarks. Key findings include:
- Performance on General and Fine-Grained Benchmarks: ConvLLaVA outperforms LLaVA-1.5 13B on various benchmarks, including SEEDBench, RealWorldQA, and TextVQA, among others.
- Efficiency and Flexibility: ConvLLaVA is capable of efficiently handling images of arbitrary aspect ratios and resolutions. The model demonstrates flexibility by processing images with different resolutions during training and inference.
Theoretical and Practical Implications
The theoretical implications of this research are substantial. The hierarchical backbone's ability to compress high-resolution visual information challenges the conventional reliance on ViT and its quadratic spatial complexity. The research suggests a shift towards hierarchical backbones for future LMMs due to their superior performance in handling high-resolution images efficiently.
On the practical side, ConvLLaVA's efficiency in processing high-resolution images opens new avenues for applications requiring detailed visual understanding, such as medical imaging, autonomous driving, and high-definition video analysis. The reduction in computational load without sacrificing performance makes ConvLLaVA a promising candidate for real-time and resource-constrained environments.
Future Directions
Several future research directions are proposed:
- Designing Specialized High-Resolution Encoders: While ConvNeXt has shown promise, designing a visual encoder specifically optimized for high-resolution tasks could further enhance performance.
- Balancing Compression and Information Retrieval: Future work should explore the trade-offs between compressing visual information to reduce token count and retaining sufficient detail for effective retrieval and analysis by the LLM.
- Exploring Other Hierarchical Structures: Investigating other hierarchical architectures and their potential benefits for LMMs could lead to even more efficient and capable multimodal models.
Conclusion
ConvLLaVA represents a significant step forward in the development of efficient high-resolution LMMs. By leveraging a hierarchical backbone and introducing key optimizations, the model addresses the critical issues of excessive visual tokens and computational complexity. The research provides a compelling case for the adoption of hierarchical backbones in future LMMs, emphasizing their efficiency and superior performance in handling high-resolution visual information.
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