MTMamba++: Enhancing Multi-Task Dense Scene Understanding via Mamba-Based Decoders
<|reference_start|>MTMamba++: Enhancing Multi-Task Dense Scene Understanding via Mamba-Based Decoders: Multi-task dense scene understanding, which trains a model for multiple dense prediction tasks, has a wide range of application scenarios. Capturing long-range dependency and enhancing cross-task interactions are crucial to multi-task dense prediction. In this paper, we propose MTMamba++, a novel architecture for multi-task scene understanding featuring with a Mamba-based decoder. It contains two types of core blocks: self-task Mamba (STM) block and cross-task Mamba (CTM) block. STM handles long-range dependency by leveraging state-space models, while CTM explicitly models task interactions to facilitate information exchange across tasks. We design two types of CTM block, namely F-CTM and S-CTM, to enhance cross-task interaction from feature and semantic perspectives, respectively. Experiments on NYUDv2, PASCAL-Context, and Cityscapes datasets demonstrate the superior performance of MTMamba++ over CNN-based and Transformer-based methods. The code is available at https://github.com/EnVision-Research/MTMamba.<|reference_end|>
@article{lin2024mtmamba++:,
    title={MTMamba++: Enhancing Multi-Task Dense Scene Understanding via
  Mamba-Based Decoders},
    author={Baijiong Lin, Weisen Jiang, Pengguang Chen, Shu Liu, and Ying-Cong
  Chen},
    journal={arXiv preprint arXiv:2408.15101},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.15101},
    primaryClass={cs.CV cs.AI}
}