# MemoryMamba: Memory-Augmented State Space Model for Defect Recognition

arXiv: https://arxiv.org/abs/2405.03673 · HTML: https://mlsanity.com/paper/2405.03673/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 63 references that are in the map.
- First posted on arXiv 6 May 2024.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [VMamba: Visual State Space Model](https://mlsanity.com/paper/2401.10166/) (2024)
- [A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect](https://mlsanity.com/paper/2401.16402/) (2024)
- [State Space Model for New-Generation Network Alternative to Transformers: A Survey](https://mlsanity.com/paper/2404.09516/) (2024)
- [Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review](https://mlsanity.com/paper/2402.10350/) (2024)
- [UltraLight VM-UNet: Parallel Vision Mamba Significantly Reduces Parameters for Skin Lesion Segmentation](https://mlsanity.com/paper/2403.20035/) (2024)
- [Towards Robust Ranker for Text Retrieval](https://mlsanity.com/paper/2206.08063/) (2022)
- [Fine-Grained Distillation for Long Document Retrieval](https://mlsanity.com/paper/2212.10423/) (2022)
- [Bridging the Domain Gap: Self-Supervised 3D Scene Understanding with Foundation Models](https://mlsanity.com/paper/2305.08776/) (2023)
- [Multimodal Semi-Supervised Learning for 3D Objects](https://mlsanity.com/paper/2110.11601/) (2021)
- [Residual-based Language Models are Free Boosters for Biomedical Imaging](https://mlsanity.com/paper/2403.17343/) (2024)
- [Particle Filter SLAM for Vehicle Localization](https://mlsanity.com/paper/2402.07429/) (2024)
- [Adaptive Ensembles of Fine-Tuned Transformers for LLM-Generated Text Detection](https://mlsanity.com/paper/2403.13335/) (2024)
- [Multimodal Event Transformer for Image-guided Story Ending Generation](https://mlsanity.com/paper/2301.11357/) (2023)
- [Style-Aware Contrastive Learning for Multi-Style Image Captioning](https://mlsanity.com/paper/2301.11367/) (2023)
- [Convolutional State Space Models for Long-Range Spatiotemporal Modeling](https://mlsanity.com/paper/2310.19694/) (2023)
- [SST: Multi-Scale Hybrid Mamba-Transformer Experts for Time Series Forecasting](https://mlsanity.com/paper/2404.14757/) (2024)
- [News Recommendation with Attention Mechanism](https://mlsanity.com/paper/2402.07422/) (2024)
- [Class-Level Confidence Based 3D Semi-Supervised Learning](https://mlsanity.com/paper/2210.10138/) (2022)
- [InsectMamba: Insect Pest Classification with State Space Model](https://mlsanity.com/paper/2404.03611/) (2024)
- [Unsupervised machine learning via transfer learning and k-means clustering to classify materials image data](https://mlsanity.com/paper/2007.08361/) (2020)
- [Recognition Of Surface Defects On Steel Sheet Using Transfer Learning](https://mlsanity.com/paper/1909.03258/) (2019)
- [Recognition of Defective Mineral Wool Using Pruned ResNet Models](https://mlsanity.com/paper/2211.00466/) (2022)
- [Automated Defect Recognition of Castings defects using Neural Networks](https://mlsanity.com/paper/2209.02279/) (2022)

## Built on by (most cited first, 24 of 24)

- [Vision Mamba: A Comprehensive Survey and Taxonomy](https://mlsanity.com/paper/2405.04404/) (2024)
- [A Survey of Mamba](https://mlsanity.com/paper/2408.01129/) (2024)
- [TheraMind: A Strategic and Adaptive Agent for Longitudinal Psychological Counseling](https://mlsanity.com/paper/2510.25758/) (2025)
- [End-to-End Chart Summarization via Visual Chain-of-Thought in Vision-Language Models](https://mlsanity.com/paper/2502.17589/) (2025)
- [Demystifying Low-Rank Knowledge Distillation in Large Language Models: Convergence, Generalization, and Information-Theoretic Guarantees](https://mlsanity.com/paper/2603.22355/) (2026)
- [MHS-VM: Multi-Head Scanning in Parallel Subspaces for Vision Mamba](https://mlsanity.com/paper/2406.05992/) (2024)
- [Optimizing Psychological Counseling with Instruction-Tuned Large Language Models](https://mlsanity.com/paper/2406.13617/) (2024)
- [Improving Visual Storytelling with Multimodal Large Language Models](https://mlsanity.com/paper/2407.02586/) (2024)
- [Enhancing Scientific Figure Captioning Through Cross-modal Learning](https://mlsanity.com/paper/2406.17047/) (2024)
- [Fault Diagnosis in Power Grids with Large Language Model](https://mlsanity.com/paper/2407.08836/) (2024)
- [Enhancing Agricultural Machinery Management through Advanced LLM Integration](https://mlsanity.com/paper/2407.20588/) (2024)
- [DocRefine: An Intelligent Framework for Scientific Document Understanding and Content Optimization based on Multimodal Large Model Agents](https://mlsanity.com/paper/2508.07021/) (2025)
- [Robust Diagram Reasoning: A Framework for Enhancing LVLM Performance on Visually Perturbed Scientific Diagrams](https://mlsanity.com/paper/2508.16972/) (2025)
- [LLMs for Enhanced Agricultural Meteorological Recommendations](https://mlsanity.com/paper/2408.04640/) (2024)
- [Enhancing AI-Driven Psychological Consultation: Layered Prompts with Large Language Models](https://mlsanity.com/paper/2408.16276/) (2024)
- [Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection](https://mlsanity.com/paper/2412.00890/) (2024)
- [Selective State Space Memory for Large Vision-Language Models](https://mlsanity.com/paper/2412.09875/) (2024)
- [Cross-Domain Semantic Segmentation with Large Language Model-Assisted Descriptor Generation](https://mlsanity.com/paper/2501.16467/) (2025)
- [MM-FusionNet: Context-Aware Dynamic Fusion for Multi-modal Fake News Detection with Large Vision-Language Models](https://mlsanity.com/paper/2508.05684/) (2025)
- [Context-Enhanced Granular Edit Representation for Efficient and Accurate ASR Post-editing](https://mlsanity.com/paper/2509.14263/) (2025)
- [AE-LLM: Adaptive Efficiency Optimization for Large Language Models](https://mlsanity.com/paper/2603.20492/) (2026)
- [HierBias: Context-Conditioned Hierarchical Media Bias Detection with Multi-Task Type Classification](https://mlsanity.com/paper/2606.26100/) (2026)
- [Spectral Origins of the Self-Correction Blind Spot in Autoregressive Generation](https://mlsanity.com/paper/2607.09803/) (2026)
- [When Does External Guidance Help LLM Reasoning? A Bias-Variance Theory of Guidance-Augmented GRPO](https://mlsanity.com/paper/2610.06861/) (2026)

## Abstract (arXiv)

As automation advances in manufacturing, the demand for precise and sophisticated defect detection technologies grows. Existing vision models for defect recognition methods are insufficient for handling the complexities and variations of defects in contemporary manufacturing settings. These models especially struggle in scenarios involving limited or imbalanced defect data. In this work, we introduce MemoryMamba, a novel memory-augmented state space model (SSM), designed to overcome the limitations of existing defect recognition models. MemoryMamba integrates the state space model with the memory augmentation mechanism, enabling the system to maintain and retrieve essential defect-specific information in training. Its architecture is designed to capture dependencies and intricate defect characteristics, which are crucial for effective defect detection. In the experiments, MemoryMamba was evaluated across four industrial datasets with diverse defect types and complexities. The model consistently outperformed other methods, demonstrating its capability to adapt to various defect recognition scenarios.