Multi-Scale Feature Attention Network for Polymer Classification Using Terahertz Spectroscopy

• URL: http://arxiv.org/abs/2606.06554v2
• Date: Mon, 08 Jun 2026 07:48:55 GMT
• Title: Multi-Scale Feature Attention Network for Polymer Classification Using Terahertz Spectroscopy
• Abstract summary: Multi-Scale Feature Attention Network (MSFAN) is a novel deep learning architecture tailored for Terahertz (THz) data.<n>MSFAN consistently outperforms state-of-the-art models, reaching a classification accuracy of 85.2%.<n>This study demonstrates the potential of combining THz spectroscopy with deep learning techniques for effective, scalable, and interpretable polymer classification.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reliable polymer identification is essential for ensuring the quality and safety of recycled plastics, yet conventional sorting and spectroscopic techniques often struggle to deliver robust discrimination. Terahertz (THz) spectroscopy offers a promising alternative, providing high-resolution and non-destructive measurements. In this work, we leverage THz signals to classify 12 types of polymers, including pure polymers, multilayer films, commercial blends, and biopolymers. To handle the complexity of these spectral signals, we propose the Multi-Scale Feature Attention Network (MSFAN), a novel deep learning architecture tailored for THz data. The framework integrates feature gating for signal recalibration and multi-scale parallel convolutions to capture diverse frequency patterns. These features are further refined through cross-feature attention and attention pooling, enabling the model to intrinsically highlight the most informative THz regions. MSFAN consistently outperforms state-of-the-art models, reaching a classification accuracy of 85.2%. This study demonstrates the potential of combining THz spectroscopy with deep learning techniques for effective, scalable, and interpretable polymer classification.

Related papers

• MM-Spectrum: Multimodal Multi-spectral Molecular Structural Elucidation with a Stable MoE Framework [9.391427657197923]
  MM-Spectrum is a sparse Mixture-of-Experts framework tailored for multimodal multispectral spectra-to-structure elucidation.<n>It incorporates shared and interaction experts, together with heterogeneous expert capacities, to extract multispectral modality-unique and cross-modal synergistic information.<n> MM-Spectrum achieves consistent and substantial improvements, supported by ablation studies and interpretability analyses.
  arXiv  Detail & Related papers  (2026-08-27T15:55:30Z)
• Quantum Enchanced Multi-Scale CNN with Bi-directional Mamba for Crop Field Analysis [0.06372261626436675]
  Hyperspectral image (HSI) crop analysis is essential for precision agriculture.<n>HSI classification remains challenging due to high spectral dimensionality, spatial complexity, class imbalance, and limited labeled samples.<n>This paper proposes a BiSpectral Mamba-based framework that combines multi-scale convolutional feature extraction, spectral attention, bidirectional state-space modeling, and quantum-inspired learning.
  arXiv  Detail & Related papers  (2026-06-15T19:11:09Z)
• An Enhanced Geometric-Spectral Feature Learning Framework for Airborne Multispectral Point Cloud Classification [51.004475033276215]
  We build two MPC datasets and propose an enhanced geometric-spectral feature learning framework based on attentions for airborne MPC classification.<n>A key component in our model is a two-stream feature fusion method with attention mechanisms, which enhances the representation capability of spatial-spectral features.<n> Experimental results on two airborne MPC datasets demonstrate the effectiveness of the proposed method compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-06-08T07:18:38Z)
• DSXFormer: Dual-Pooling Spectral Squeeze-Expansion and Dynamic Context Attention Transformer for Hyperspectral Image Classification [4.361601680177976]
  DSXFormer is a novel dual-pooling squeeze-expansion transformer with Dynamic Context Attention for HSIC.<n>It consistently outperforms state-of-the-art methods, achieving classification accuracies of 99.95%, 98.91%, 99.85%, and 98.52%, respectively.
  arXiv  Detail & Related papers  (2026-02-02T10:12:18Z)
• Breaking the Modality Barrier: Generative Modeling for Accurate Molecule Retrieval from Mass Spectra [60.08608779794957]
  We propose GLMR, a Generative Language Model-based Retrieval framework.<n>In the pre-retrieval stage, a contrastive learning-based model identifies top candidate molecules as contextual priors for the input mass spectrum.<n>In the generative retrieval stage, these candidate molecules are integrated with the input mass spectrum to guide a generative model in producing refined molecular structures.
  arXiv  Detail & Related papers  (2025-11-09T07:25:53Z)
• Spatial-Spectral Binarized Neural Network for Panchromatic and Multi-spectral Images Fusion [25.15016853820625]
  Deep learning models have achieved excellent performance, but they often come with high computational complexity.<n>In this paper, we explore the feasibility of applying the binary neural network (BNN) to pan-sharpening.<n>A series of S2B-Conv form a brand-new binary network for pan-sharpening, dubbed as S2BNet.
  arXiv  Detail & Related papers  (2025-09-27T14:10:51Z)
• SpectrumFM: Redefining Spectrum Cognition via Foundation Modeling [65.65474629224558]
  We propose a spectrum foundation model, termed SpectrumFM, which provides a new paradigm for spectrum cognition.<n>An innovative spectrum encoder that exploits the convolutional neural networks is proposed to effectively capture both fine-grained local signal structures and high-level global dependencies in the spectrum data.<n>Two novel self-supervised learning tasks, namely masked reconstruction and next-slot signal prediction, are developed for pre-training SpectrumFM, enabling the model to learn rich and transferable representations.
  arXiv  Detail & Related papers  (2025-08-02T14:40:50Z)
• LUMIR: an LLM-Driven Unified Agent Framework for Multi-task Infrared Spectroscopy Reasoning [12.138903544219724]
  This study introduces LUMIR, a framework designed to achieve accurate infrared spectral analysis under low data conditions.<n> LUMIR integrates a structured literature knowledge base, automated preprocessing, feature extraction, and predictive modeling into a unified pipeline.<n>It was validated on diverse datasets, including the publicly available Milk near-infrared dataset, Chinese medicinal herbs, Citri Reticulatae Pericarpium(CRP) with different storage durations, an industrial wastewater COD dataset, Tecator and Corn.
  arXiv  Detail & Related papers  (2025-07-29T03:20:51Z)
• DiffSpectra: Molecular Structure Elucidation from Spectra using Diffusion Models [68.19129717255053]
  We present DiffSpectra, a generative framework that formulates molecular structure elucidation as a conditional generation process.<n>Our experiments demonstrate that DiffSpectra accurately elucidates molecular structures, achieving 40.76% top-1 and 99.49% top-10 accuracy.
  arXiv  Detail & Related papers  (2025-07-09T13:57:20Z)
• Self-Learning Hyperspectral and Multispectral Image Fusion via Adaptive Residual Guided Subspace Diffusion Model [17.94165288907444]
  Hyperspectral and multispectral image (HSI-MSI) fusion involves combining a low-resolution hyperspectral image (LR-HSI) with a high-resolution multispectral image (HR-MSI) to generate a high-resolution hyperspectral image (HR-HSI)
  arXiv  Detail & Related papers  (2025-05-17T03:05:13Z)
• SpectrumFM: A Foundation Model for Intelligent Spectrum Management [99.08036558911242]
  Existing intelligent spectrum management methods, typically based on small-scale models, suffer from notable limitations in recognition accuracy, convergence speed, and generalization.<n>This paper proposes a novel spectrum foundation model, termed SpectrumFM, establishing a new paradigm for spectrum management.<n>Experiments demonstrate that SpectrumFM achieves superior performance in terms of accuracy, robustness, adaptability, few-shot learning efficiency, and convergence speed.
  arXiv  Detail & Related papers  (2025-05-02T04:06:39Z)
• CARL: Camera-Agnostic Representation Learning for Spectral Image Analysis [69.02751635551724]
  Spectral imaging offers promising applications across diverse domains, including medicine and urban scene understanding.<n> variability in channel dimensionality and captured wavelengths among spectral cameras impede the development of AI-driven methodologies.<n>We introduce CARL, a model for Camera-Agnostic Representation Learning across RGB, multispectral, and hyperspectral imaging modalities.
  arXiv  Detail & Related papers  (2025-04-27T13:06:40Z)
• PAD: Phase-Amplitude Decoupling Fusion for Multi-Modal Land Cover Classification [49.37555541088792]
  Phase-Amplitude Decoupling (PAD) is a frequency-aware framework that separates phase (modality-shared) and amplitude (modality-complementary) components.<n>This work establishes a new paradigm for physics-aware multi-modal fusion in remote sensing.
  arXiv  Detail & Related papers  (2025-04-27T07:21:42Z)
• Machine Learning for Exoplanet Detection in High-Contrast Spectroscopy: Revealing Exoplanets by Leveraging Hidden Molecular Signatures in Cross-Correlated Spectra with Convolutional Neural Networks [0.0]
  Cross-correlation for spectroscopy uses molecular templates to isolate a planet's spectrum from its host star. We introduce machine learning for cross-correlation spectroscopy (MLCCS) The method aims to leverage weak assumptions on exoplanet characterisation, such as the presence of specific molecules in atmospheres, to improve detection sensitivity for exoplanets.
  arXiv  Detail & Related papers  (2024-05-22T09:25:58Z)
• Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers [124.01928050651466]
  We propose a new type of polyp segmentation method, named Polyp-PVT. The proposed model, named Polyp-PVT, effectively suppresses noises in the features and significantly improves their expressive capabilities.
  arXiv  Detail & Related papers  (2021-08-16T07:09:06Z)
• A Comparative study of Artificial Neural Networks Using Reinforcement learning and Multidimensional Bayesian Classification Using Parzen Density Estimation for Identification of GC-EIMS Spectra of Partially Methylated Alditol Acetates [0.304585143845864]
  This study reports the development of a pattern recognition search engine for a World Wide Web-based database of gas chromatography-electron impact mass spectra (GC-EIMS) of partially methylated Alditol acetates (PMAAs) The developed system is implemented on the world wide web, and is intended to identify PMAAs using submitted spectra of these molecules recorded on any GC-EIMS instrument.
  arXiv  Detail & Related papers  (2020-07-31T17:54:51Z)
• Hyperspectral-Multispectral Image Fusion with Weighted LASSO [68.04032419397677]
  We propose an approach for fusing hyperspectral and multispectral images to provide high-quality hyperspectral output. We demonstrate that the proposed sparse fusion and reconstruction provides quantitatively superior results when compared to existing methods on publicly available images.
  arXiv  Detail & Related papers  (2020-03-15T23:07:56Z)

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.