ConvLLaVA: Hierarchical Backbones as Visual Encoder for Large Multimodal Models

TL;DR

ConvLLaVA introduces a hierarchical visual encoder using ConvNeXt to efficiently process high-resolution images in large multimodal models, reducing redundancy and computational complexity while maintaining competitive performance.

Contribution

It replaces Vision Transformers with ConvNeXt in LMMs, employing optimizations to handle high-resolution images effectively and reduce visual token redundancy.

Findings

Supports 1536x1536 resolution with only 576 visual tokens

Achieves competitive results on mainstream benchmarks

Reduces computational complexity compared to existing models

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.