  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.
