Summary of Mtmamba++: Enhancing Multi-task Dense Scene Understanding Via Mamba-based Decoders, by Baijiong Lin et al.
MTMamba++: Enhancing Multi-Task Dense Scene Understanding via Mamba-Based Decoders
by Baijiong Lin, Weisen Jiang, Pengguang Chen, Shu Liu, Ying-Cong Chen
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed MTMamba++ architecture is a novel approach to multi-task dense scene understanding, capable of capturing long-range dependencies and enhancing cross-task interactions. This model features a Mamba-based decoder with two types of core blocks: self-task Mamba (STM) blocks for handling long-range dependency using state-space models, and cross-task Mamba (CTM) blocks for facilitating information exchange across tasks from feature and semantic perspectives. The model is evaluated on NYUDv2, PASCAL-Context, and Cityscapes datasets, demonstrating superior performance compared to CNN-based and Transformer-based methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multi-task dense scene understanding trains a model for multiple prediction tasks. This paper proposes MTMamba++, an architecture that captures long-range dependencies and enhances task interactions. The model has two types of blocks: self-task Mamba (STM) and cross-task Mamba (CTM). STM handles long-range dependency, while CTM helps tasks share information. The model is tested on several datasets and outperforms other methods. |
Keywords
» Artificial intelligence » Cnn » Decoder » Multi task » Scene understanding » Transformer
