mlsanityData to 14 Oct 2026

RAUM-Net: Regional Attention and Uncertainty-aware Mamba Network

arXiv 2506.21905 ↗First posted 27 Jun 2025cs.CVcited by 0

On the map: State-space sequence models (S4, Mamba) → Vision Mamba backbones

Abstract (arXiv)

Fine Grained Visual Categorization (FGVC) remains a challenging task in computer vision due to subtle inter class differences and fragile feature representations. Existing methods struggle in fine grained scenarios, especially when labeled data is scarce. We propose a semi supervised method combining Mamba based feature modeling, region attention, and Bayesian uncertainty. Our approach enhances local to global feature modeling while focusing on key areas during learning. Bayesian inference selects high quality pseudo labels for stability. Experiments show strong performance on FGVC benchmarks with occlusions, demonstrating robustness when labeled data is limited. Code is available at https://github.com/wxqnl/RAUM Net.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Vision Mamba backbones, one of 10 directions in State-space sequence models (S4, Mamba). Builds on 21 of its 25 references that are in the map. First posted on arXiv 27 Jun 2025.

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21 of 25 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025Dynamic Vision Mamba7 Apr

Other references · 20

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  4. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  5. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,085
  6. 2021Swin Transformer V2: Scaling Up Capacity and Resolutioncited by 1,228
  7. 2024VMamba: Visual State Space Modelcited by 969
  8. 2021FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labelingcited by 540
  9. 2021End-to-End Semi-Supervised Object Detection with Soft Teachercited by 316
  10. 2021In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learningcited by 301
  11. 2022FreeMatch: Self-adaptive Thresholding for Semi-supervised Learningcited by 232
  12. 2020An Overview of Deep Semi-Supervised Learningcited by 121
8 more
  1. 2018Learning to Navigate for Fine-grained Classificationcited by 103
  2. 2015Part-Stacked CNN for Fine-Grained Visual Categorizationcited by 62
  3. 2020DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentationcited by 54
  4. 2016Low-rank Bilinear Pooling for Fine-Grained Classificationcited by 42
  5. 2025Uncertainty-guided Perturbation for Image Super-Resolution Diffusion Modelcited by 29
  6. 2020Find it if You Can: End-to-End Adversarial Erasing for Weakly-Supervised Semantic Segmentationcited by 4
  7. 2021Fine-Grained Adversarial Semi-supervised Learningcited by 4
  8. 2025AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active Learningcited by 1

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  1. 2024Vision Mamba Distillation for Low-resolution Fine-grained Image Classification
  2. 2023Concept Drift and Long-Tailed Distribution in Fine-Grained Visual Categorization: Benchmark and Method
  3. 2024Data-free Knowledge Distillation for Fine-grained Visual Categorization
  4. 2024Extract More from Less: Efficient Fine-Grained Visual Recognition in Low-Data Regimes
  5. 2026Benchmarking Large Vision-Language Models on Fine-Grained Image Tasks: From Evaluation to Diagnosis
  6. 2024African or European Swallow? Benchmarking Large Vision-Language Models for Fine-Grained Object Classification
  7. 2026PAND: Prompt-Aware Neighborhood Distillation for Lightweight Fine-Grained Visual Classification
  8. 2025Benchmarking Large Vision-Language Models on Fine-Grained Image Tasks: A Comprehensive Evaluation