|
|
|
|
| LEADER |
01000caa a22002652c 4500 |
| 001 |
NLM393512487 |
| 003 |
DE-627 |
| 005 |
20251007231851.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
251003s2025 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TPAMI.2025.3593621
|2 doi
|
| 028 |
5 |
2 |
|a pubmed25n1591.xml
|
| 035 |
|
|
|a (DE-627)NLM393512487
|
| 035 |
|
|
|a (NLM)40729720
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Lin, Baijiong
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a MTMamba++
|b Enhancing Multi-Task Dense Scene Understanding via Mamba-Based Decoders
|
| 264 |
|
1 |
|c 2025
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Revised 06.10.2025
|
| 500 |
|
|
|a published: Print
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a 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. Extensive experiments on NYUDv2, PASCAL-Context, and Cityscapes datasets demonstrate the superior performance of MTMamba++ over CNN-based, Transformer-based, and diffusion-based methods while maintaining high computational efficiency
|
| 650 |
|
4 |
|a Journal Article
|
| 700 |
1 |
|
|a Jiang, Weisen
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Chen, Pengguang
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Liu, Shu
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Chen, Ying-Cong
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 47(2025), 11 vom: 28. Okt., Seite 10633-10645
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnas
|
| 773 |
1 |
8 |
|g volume:47
|g year:2025
|g number:11
|g day:28
|g month:10
|g pages:10633-10645
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TPAMI.2025.3593621
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 47
|j 2025
|e 11
|b 28
|c 10
|h 10633-10645
|