MTMamba: Enhancing Multi-Task Dense Scene Understanding by Mamba-Based Decoders
<|reference_start|>MTMamba: Enhancing Multi-Task Dense Scene Understanding by Mamba-Based Decoders: Multi-task dense scene understanding, which learns a model for multiple dense prediction tasks, has a wide range of application scenarios. Modeling long-range dependency and enhancing cross-task interactions are crucial to multi-task dense prediction. In this paper, we propose MTMamba, a novel Mamba-based architecture for multi-task scene understanding. 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 Mamba, while CTM explicitly models task interactions to facilitate information exchange across tasks. Experiments on NYUDv2 and PASCAL-Context datasets demonstrate the superior performance of MTMamba over Transformer-based and CNN-based methods. Notably, on the PASCAL-Context dataset, MTMamba achieves improvements of +2.08, +5.01, and +4.90 over the previous best methods in the tasks of semantic segmentation, human parsing, and object boundary detection, respectively. The code is available at https://github.com/EnVision-Research/MTMamba.<|reference_end|>
@article{lin2024mtmamba:,
    title={MTMamba: Enhancing Multi-Task Dense Scene Understanding by Mamba-Based
  Decoders},
    author={Baijiong Lin, Weisen Jiang, Pengguang Chen, Yu Zhang, Shu Liu, and
  Ying-Cong Chen},
    journal={arXiv preprint arXiv:2407.02228},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02228},
    primaryClass={cs.CV cs.AI}
}