The state space models, employing recursively propagated features,
demonstrate strong representation capabilities comparable to Transformer models
and superior efficiency. However, constrained by the inherent geometric
constraints of sequences, it still falls short in modeling long-range
dependencies. To address this issue, we propose the GrootVL network, which
first dynamically generates a tree topology based on spatial relationships and
input features. Then, feature propagation is performed based on this graph,
thereby breaking the original sequence constraints to achieve stronger
representation capabilities. Additionally, we introduce a linear complexity
dynamic programming algorithm to enhance long-range interactions without
increasing computational cost. GrootVL is a versatile multimodal framework that
can be applied to both visual and textual tasks. Extensive experiments
demonstrate that our method significantly outperforms existing structured state
space models on image classification, object detection and segmentation.
Besides, by fine-tuning large language models, our approach achieves consistent
improvements in multiple textual tasks at minor training cost.

借助于动态生成的树形拓扑结构和特征传播，GrootVL 网络可超越传统状态空间模型在建模长距离依赖关系上的局限，具备强大的表示能力；此外，通过引入线性复杂度的动态规划算法，还能在不增加计算成本的情况下增强长距离交互，并在图像分类、目标检测、分割以及文本任务中取得显著改进。