Metodebevisregister

Vision Mamba

Vision Mamba is an efficient state space model approach for image understanding introduced in 2024 that adapts Mamba, a linear-complexity sequence model, to computer vision. By reformulating image tokens as sequences and using state space models, Vision Mamba achieves competitive accuracy with transformers while maintaining linear computational complexity.

Sources recorded, not reviewed

Kilderegister

Siteringer kopiert ordrett fra metodens kilderegister. Ingen påstandsnivåverifisering er underforstått fra dem.

Vision Mamba: Efficient State Space Models for Image Understanding

Taksonomisk metoderegister · ml-model / deep-learning

Zhu, L., Liao, B., Zhang, Q., Wang, X., Liu, W., & Wang, X. (2024). Vision Mamba: Efficient state space models for image understanding. In International Conference on Machine Learning. · URL

Åpne full metode

Kuraterte påstander

Påstander lagret i bevishovedboken, hver med sin egen vurdering.

Ingen kuraterte påstander ennå

Denne visningen finner ikke opp en påstandsvurdering når hovedboken ikke har noen.

Relaterte metoder

Generert fra metodegrafen og vist som maskinforslåtte relasjoner – ingen bevispåstand er underforstått.

Same method familyMamba (State Space Model)machine-suggested · Relational suggestion, not evidence.Same method familySpatial-Temporal GCNmachine-suggested · Relational suggestion, not evidence.Same method familySwin Transformermachine-suggested · Relational suggestion, not evidence.Same method familyVision Transformermachine-suggested · Relational suggestion, not evidence.