MTMamba++: Enhancing Multi-Task Dense Scene Understanding via Mamba-Based Decoders

• URL: http://arxiv.org/abs/2408.15101v1
• Date: Tue, 27 Aug 2024 14:36:46 GMT
• Title: MTMamba++: Enhancing Multi-Task Dense Scene Understanding via Mamba-Based Decoders
• Authors: Baijiong Lin, Weisen Jiang, Pengguang Chen, Shu Liu, Ying-Cong Chen,
• Abstract summary: 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 block and cross-task Mamba block. Experiments on NYUDv2, PASCAL-Context, and Cityscapes datasets demonstrate the superior performance of MTMamba++ over CNN-based and Transformer-based methods.
• Score: 26.236118242986805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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