Visual Attention Exploration in Vision-Based Mamba Models

• URL: http://arxiv.org/abs/2502.20764v1
• Date: Fri, 28 Feb 2025 06:33:18 GMT
• Title: Visual Attention Exploration in Vision-Based Mamba Models
• Authors: Junpeng Wang, Chin-Chia Michael Yeh, Uday Singh Saini, Mahashweta Das,
• Abstract summary: State space models (SSMs) have emerged as an efficient alternative to transformer-based models.<n>One of the latest advances in SSMs, Mamba, introduces a selective scan mechanism that assigns trainable weights to input tokens.<n>Mamba has also been successfully extended to the vision domain by decomposing 2D images into smaller patches and arranging them as 1D sequences.
• Score: 13.931745986906769
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: State space models (SSMs) have emerged as an efficient alternative to transformer-based models, offering linear complexity that scales better than transformers. One of the latest advances in SSMs, Mamba, introduces a selective scan mechanism that assigns trainable weights to input tokens, effectively mimicking the attention mechanism. Mamba has also been successfully extended to the vision domain by decomposing 2D images into smaller patches and arranging them as 1D sequences. However, it remains unclear how these patches interact with (or attend to) each other in relation to their original 2D spatial location. Additionally, the order used to arrange the patches into a sequence also significantly impacts their attention distribution. To better understand the attention between patches and explore the attention patterns, we introduce a visual analytics tool specifically designed for vision-based Mamba models. This tool enables a deeper understanding of how attention is distributed across patches in different Mamba blocks and how it evolves throughout a Mamba model. Using the tool, we also investigate the impact of different patch-ordering strategies on the learned attention, offering further insights into the model's behavior.

Related papers

• DefMamba: Deformable Visual State Space Model [65.50381013020248]
  We propose a novel visual foundation model called DefMamba. By combining a deformable scanning(DS) strategy, this model significantly improves its ability to learn image structures and detects changes in object details. Numerous experiments have shown that DefMamba achieves state-of-the-art performance in various visual tasks.
  arXiv  Detail & Related papers  (2025-04-08T08:22:54Z)
• Dynamic Vision Mamba [41.84910346271891]
  Mamba-based vision models have gained extensive attention as a result of being computationally more efficient than attention-based models. For token redundancy, we analytically find that early token pruning methods will result in inconsistency between training and inference. For block redundancy, we allow each image to select SSM blocks dynamically based on an empirical observation that the inference speed of Mamba-based vision models is largely affected by the number of SSM blocks.
  arXiv  Detail & Related papers  (2025-04-07T07:31:28Z)
• V2M: Visual 2-Dimensional Mamba for Image Representation Learning [68.51380287151927]
  Mamba has garnered widespread attention due to its flexible design and efficient hardware performance to process 1D sequences. Recent studies have attempted to apply Mamba to the visual domain by flattening 2D images into patches and then regarding them as a 1D sequence. We propose a Visual 2-Dimensional Mamba model as a complete solution, which directly processes image tokens in the 2D space.
  arXiv  Detail & Related papers  (2024-10-14T11:11:06Z)
• MambaVision: A Hybrid Mamba-Transformer Vision Backbone [54.965143338206644]
  We propose a novel hybrid Mamba-Transformer backbone, denoted as MambaVision, which is specifically tailored for vision applications. Our core contribution includes redesigning the Mamba formulation to enhance its capability for efficient modeling of visual features. We conduct a comprehensive ablation study on the feasibility of integrating Vision Transformers (ViT) with Mamba.
  arXiv  Detail & Related papers  (2024-07-10T23:02:45Z)
• Demystify Mamba in Vision: A Linear Attention Perspective [72.93213667713493]
  Mamba is an effective state space model with linear computation complexity.<n>We show that Mamba shares surprising similarities with linear attention Transformer.<n>We propose a Mamba-Inspired Linear Attention (MILA) model by incorporating the merits of these two key designs into linear attention.
  arXiv  Detail & Related papers  (2024-05-26T15:31:09Z)
• The Hidden Attention of Mamba Models [54.50526986788175]
  The Mamba layer offers an efficient selective state space model (SSM) that is highly effective in modeling multiple domains. We show that such models can be viewed as attention-driven models. This new perspective enables us to empirically and theoretically compare the underlying mechanisms to that of the self-attention layers in transformers.
  arXiv  Detail & Related papers  (2024-03-03T18:58:21Z)
• STMT: A Spatial-Temporal Mesh Transformer for MoCap-Based Action Recognition [50.064502884594376]
  We study the problem of human action recognition using motion capture (MoCap) sequences. We propose a novel Spatial-Temporal Mesh Transformer (STMT) to directly model the mesh sequences. The proposed method achieves state-of-the-art performance compared to skeleton-based and point-cloud-based models.
  arXiv  Detail & Related papers  (2023-03-31T16:19:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.