Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
131 tokens/sec
GPT-4o
10 tokens/sec
Gemini 2.5 Pro Pro
47 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

MAL: Cluster-Masked and Multi-Task Pretraining for Enhanced xLSTM Vision Performance (2412.10730v1)

Published 14 Dec 2024 in cs.CV

Abstract: The Long Short-Term Memory (LSTM) networks have traditionally faced challenges in scaling and effectively capturing complex dependencies in visual tasks. The xLSTM architecture has emerged to address these limitations, incorporating exponential gating and a parallel matrix memory structure to enhance performance and scalability. Despite these advancements, the potential of xLSTM in visual computing has not been fully realized, particularly in leveraging autoregressive techniques for improved feature extraction. In this paper, we introduce MAL (Cluster-Masked and Multi-Task Pretraining for Enhanced xLSTM Vision Performance), a novel framework that enhances xLSTM's capabilities through innovative pretraining strategies. We propose a cluster-masked masking method that significantly improves local feature capture and optimizes image scanning efficiency. Additionally, our universal encoder-decoder pretraining approach integrates multiple tasks, including image autoregression, depth estimation, and image segmentation, thereby enhancing the model's adaptability and robustness across diverse visual tasks. Our experimental results demonstrate that MAL surpasses traditional supervised models and fully leverages the scaling potential of xLSTM, setting a new benchmark in visual task performance.

Summary

We haven't generated a summary for this paper yet.