Efficient High-Resolution Visual Representation Learning with State Space Model for Human Pose Estimation Article Swipe
Hao Zhang
,
Yongqiang Ma
,
Wenqi Shao
,
Ping Luo
,
Nanning Zheng
,
Kaipeng Zhang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2410.03174
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2410.03174
Capturing long-range dependencies while preserving high-resolution visual representations is crucial for dense prediction tasks such as human pose estimation. Vision Transformers (ViTs) have advanced global modeling through self-attention but suffer from quadratic computational complexity with respect to token count, limiting their efficiency and scalability to high-resolution inputs, especially on mobile and resource-constrained devices. State Space Models (SSMs), exemplified by Mamba, offer an efficient alternative by combining global receptive fields with linear computational complexity, enabling scalable and resource-friendly sequence modeling. However, when applied to dense prediction tasks, existing visual SSMs face key limitations: weak spatial inductive bias, long-range forgetting from hidden state decay, and low-resolution outputs that hinder fine-grained localization. To address these issues, we propose the Dynamic Visual State Space (DVSS) block, which augments visual state space models with multi-scale convolutional operations to enhance local spatial representations and strengthen spatial inductive biases. Through architectural exploration and theoretical analysis, we incorporate deformable operation into the DVSS block, identifying it as an efficient and effective mechanism to enhance semantic aggregation and mitigate long-range forgetting via input-dependent, adaptive spatial sampling. We embed DVSS into a multi-branch high-resolution architecture to build HRVMamba, a novel model for efficient high-resolution representation learning. Extensive experiments on human pose estimation, image classification, and semantic segmentation show that HRVMamba performs competitively against leading CNN-, ViT-, and SSM-based baselines. Code is available at https://github.com/zhanghao5201/PoseVMamba.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2410.03174
- https://arxiv.org/pdf/2410.03174
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4403885770Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2410.03174Digital Object Identifier
- Title
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Efficient High-Resolution Visual Representation Learning with State Space Model for Human Pose EstimationWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-10-04Full publication date if available
- Authors
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Hao Zhang, Yongqiang Ma, Wenqi Shao, Ping Luo, Nanning Zheng, Kaipeng ZhangList of authors in order
- Landing page
-
https://arxiv.org/abs/2410.03174Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2410.03174Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2410.03174Direct OA link when available
- Concepts
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Computer science, State (computer science), Space (punctuation), Resolution (logic), State space, High resolution, Artificial intelligence, Algorithm, Geology, Remote sensing, Mathematics, Statistics, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.sampling. | 176 |
| abstract_inverted_index.baselines. | 218 |
| abstract_inverted_index.complexity | 33 |
| abstract_inverted_index.deformable | 150 |
| abstract_inverted_index.efficiency | 41 |
| abstract_inverted_index.especially | 47 |
| abstract_inverted_index.forgetting | 97, 171 |
| abstract_inverted_index.long-range | 1, 96, 170 |
| abstract_inverted_index.operations | 131 |
| abstract_inverted_index.prediction | 12, 84 |
| abstract_inverted_index.preserving | 4 |
| abstract_inverted_index.strengthen | 138 |
| abstract_inverted_index.aggregation | 167 |
| abstract_inverted_index.alternative | 63 |
| abstract_inverted_index.complexity, | 72 |
| abstract_inverted_index.estimation, | 201 |
| abstract_inverted_index.estimation. | 18 |
| abstract_inverted_index.exemplified | 57 |
| abstract_inverted_index.experiments | 197 |
| abstract_inverted_index.exploration | 144 |
| abstract_inverted_index.identifying | 156 |
| abstract_inverted_index.incorporate | 149 |
| abstract_inverted_index.multi-scale | 129 |
| abstract_inverted_index.scalability | 43 |
| abstract_inverted_index.theoretical | 146 |
| abstract_inverted_index.Transformers | 20 |
| abstract_inverted_index.architecture | 184 |
| abstract_inverted_index.dependencies | 2 |
| abstract_inverted_index.fine-grained | 107 |
| abstract_inverted_index.limitations: | 91 |
| abstract_inverted_index.multi-branch | 182 |
| abstract_inverted_index.segmentation | 206 |
| abstract_inverted_index.architectural | 143 |
| abstract_inverted_index.competitively | 211 |
| abstract_inverted_index.computational | 32, 71 |
| abstract_inverted_index.convolutional | 130 |
| abstract_inverted_index.localization. | 108 |
| abstract_inverted_index.low-resolution | 103 |
| abstract_inverted_index.representation | 194 |
| abstract_inverted_index.self-attention | 27 |
| abstract_inverted_index.classification, | 203 |
| abstract_inverted_index.high-resolution | 5, 45, 183, 193 |
| abstract_inverted_index.representations | 7, 136 |
| abstract_inverted_index.input-dependent, | 173 |
| abstract_inverted_index.resource-friendly | 76 |
| abstract_inverted_index.resource-constrained | 51 |
| abstract_inverted_index.https://github.com/zhanghao5201/PoseVMamba. | 223 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |