Senior Research Scientist @Microsoft. Prev: Google, Microsoft, Samsung, Harman, L&T; @KU Leuven, @IITK, @IITB .
Mamba360 Daily Discoveries (240526): 1. Title: Demystify Mamba in Vision: A Linear Attention Perspective A. Technical Contribution: The paper provides a comprehensive analysis of the similarities and differences between Mamba and linear attention Transformers, highlighting six key design distinctions. It identifies the forget gate and block design as pivotal to Mamba's success and proposes a new model, Mamba-Like Linear Attention (MLLA), that incorporates these successful elements. B. Practical Applications: By improving the efficiency and performance of linear attention mechanisms, the MLLA model offers practical benefits for high-resolution vision tasks, such as image classification and dense prediction. This enhancement can lead to faster inference speeds and more efficient processing in real-world applications. Paper: https://lnkd.in/gSwxApBm