Hybrid CNN-Transformer Architectures

Follow this topic Ask about this topic

These architectures pair CNNs, which capture local patterns efficiently, with Transformers that model long-range relationships. Common designs use parallel CNN and ViT or Swin branches, Transformer bottlenecks in U-Nets, and gated or cross-attention fusion; some apply lightweight or linear attention to reduce computation. They are used for tasks such as medical-image segmentation, remote-sensing classification, object detection, and physiological-signal analysis, with performance measured on task-specific benchmarks and sometimes across unseen domains. The central tension is whether global context improves accuracy enough to justify added cost: gains are not universal, and well-tuned CNNs can match or outperform hybrids, especially on limited or shifted data.

Latest papers

117 papers5 papers in the last four weeks, down 67% on the four weeks before. 0.0% of all new papers.

Mar 28, 2026

Hybrid CNN-Transformer Architecture for Arabic Speech Emotion Recognition

Recognizing emotions from speech using machine learning has become an active research area due to its importance in building human-centered applications. However, while many studies have been conducted in English, German, and other European and Asian languages, research in Arabic remains scarce because of the limited availability of annotated datasets. In this paper, we present an Arabic Speech Emotion Recognition (SER) system based on a hybrid CNN-Transformer architecture. The model leverages convolutional layers to extract discriminative spectral features from Mel-spectrogram inputs and Transformer encoders to capture long-range temporal dependencies in speech. Experiments were conducted on the EYASE (Egyptian Arabic speech emotion) corpus, and the proposed model achieved 97.8% accuracy and a macro F1-score of 0.98. These results demonstrate the effectiveness of combining convolutional feature extraction with attention-based modeling for Arabic SER and highlight the potential of Transformer-based approaches in low-resource languages.

Youcef Soufiane Gheffari, Oussama Mustapha Benouddane, Samiya Silarbi

Department of Computer Science, USTO-MB, Oran, Algeria

Speech Emotion Recognition Hybrid CNN-Transformer Architectures Low-Resource Language Processing

Mar 28, 2026

Hybrid Deep Learning with Temporal Data Augmentation for Accurate Remaining Useful Life Prediction of Lithium-Ion Batteries

Accurate prediction of lithium-ion battery remaining useful life (RUL) is essential for reliable health monitoring and data-driven analysis of battery degradation. However, the robustness and generalization capabilities of existing RUL prediction models are significantly challenged by complex operating conditions and limited data availability. To address these limitations, this study proposes a hybrid deep learning model, CDFormer, which integrates convolutional neural networks, deep residual shrinkage networks, and Transformer encoders extract multiscale temporal features from battery measurement signals, including voltage, current, and capacity. This architecture enables the joint modeling of local and global degradation dynamics, effectively improving the accuracy of RUL prediction.To enhance predictive reliability, a composite temporal data augmentation strategy is proposed, incorporating Gaussian noise, time warping, and time resampling, explicitly accounting for measurement noise and variability. CDFormer is evaluated on two real-world datasets, with experimental results demonstrating its consistent superiority over conventional recurrent neural network-based and Transformer-based baselines across key metrics. By improving the reliability and predictive performance of RUL prediction from measurement data, CDFormer provides accurate and reliable forecasts, supporting effective battery health monitoring and data-driven maintenance strategies.

Yun Tian, Guili Wang, Jian Bi +7

School of Physics and Electronic Information, Anhui Normal University, Wuhu 241002, China · College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China · School of Design, NingboTech University, Ningbo 315100, China +2

RUL Estimation Lithium-Ion Batteries Hybrid CNN-Transformer Architectures Time Series Data Augmentation

Mar 26, 2026

DenseSwinV2: Channel Attentive Dual Branch CNN Transformer Learning for Cassava Leaf Disease Classification

This work presents a new Hybrid Dense SwinV2, a two-branch framework that jointly leverages densely connected convolutional features and hierarchical customized Swin Transformer V2 (SwinV2) representations for cassava disease classification. The proposed framework captures high resolution local features through its DenseNet branch, preserving the fine structural cues and also allowing for effective gradient flow. Concurrently, the customized SwinV2 models global contextual dependencies through the idea of shifted-window self attention, which enables the capture of long range interactions critical in distinguishing between visually similar lesions. Moreover, an attention channel-squeeze module is employed for each CNN Transformer stream independently to emphasize discriminative disease related responses and suppress redundant or background driven activations. Finally, these discriminative channels are fused to achieve refined representations from the dense local and SwinV2 global correlated strengthened feature maps, respectively. The proposed Dense SwinV2 utilized a public cassava leaf disease dataset of 31000 images, comprised of five diseases, including brown streak, mosaic, green mottle, bacterial blight, and normal leaf conditions. The proposed Dense SwinV2 demonstrates a significant classification accuracy of 98.02 percent with an F1 score of 97.81 percent, outperforming well-established convolutional and transformer models. These results underline the fact that Hybrid Dense SwinV2 offers robustness and practicality in the field level diagnosis of cassava disease and real world challenges related to occlusion, noise, and complex backgrounds.

Shah Saood, Saddam Hussain Khan

Artificial Intelligence Lab, Department of Computer Systems Engineering, University of Engineering and Applied Sciences · Artificial Intelligence Lab, Department of Computer Systems Engineering, University of Engineering and Applied Sciences (UEAS), Swat 19060, Pakistan · Interdisciplinary Research Center for Smart Mobility and Logistics +1

Plant Disease Classification Hybrid CNN-Transformer Architectures Vision Transformer

Mar 26, 2026

AdaSFormer: Adaptive Serialized Transformers for Monocular Semantic Scene Completion from Indoor Environments

Indoor monocular semantic scene completion (MSSC) is notably more challenging than its outdoor counterpart due to complex spatial layouts and severe occlusions. While transformers are well suited for modeling global dependencies, their high memory cost and difficulty in reconstructing fine-grained details have limited their use in indoor MSSC. To address these limitations, we introduce AdaSFormer, a serialized transformer framework tailored for indoor MSSC. Our model features three key designs: (1) an Adaptive Serialized Transformer with learnable shifts that dynamically adjust receptive fields; (2) a Center-Relative Positional Encoding that captures spatial information richness; and (3) a Convolution-Modulated Layer Normalization that bridges heterogeneous representations between convolutional and transformer features. Extensive experiments on NYUv2 and Occ-ScanNet demonstrate that AdaSFormer achieves state-of-the-art performance. The code is publicly available at: https://github.com/alanWXZ/AdaSFormer.

Xuzhi Wang, Xinran Wu, Song Wang +2

Tianjin Normal University · Zhejiang University · National University of Singapore

3D Scene Completion 3D ViTs Hybrid CNN-Transformer Architectures

Mar 25, 2026

LGEST: Dynamic Spatial-Spectral Expert Routing for Hyperspectral Image Classification

Deep learning methods, including Convolutional Neural Networks, Transformers and Mamba, have achieved remarkable success in hyperspectral image (HSI) classification. Nevertheless, existing methods exhibit inflexible integration of local-global representations, inadequate handling of spectral-spatial scale disparities across heterogeneous bands, and susceptibility to the Hughes phenomenon under high-dimensional sample heterogeneity. To address these challenges, we propose Local-Global Expert Spatial-Spectral Transformer (LGEST), a novel framework that synergistically combines three key innovations. The LGEST first employs a Deep Spatial-Spectral Autoencoder (DSAE) to generate compact yet discriminative embeddings through hierarchical nonlinear compression, preserving 3D neighborhood coherence while mitigating information loss in high-dimensional spaces. Secondly, a Cross-Interactive Mixed Expert Feature Pyramid (CIEM-FPN) leverages cross-attention mechanisms and residual mixture-of-experts layers to dynamically fuse multi-scale features, adaptively weighting spectral discriminability and spatial saliency through learnable gating functions. Finally, a Local-Global Expert System (LGES) processes decomposed features via sparsely activated expert pairs: convolutional sub-experts capture fine-grained textures, while transformer sub-experts model long-range contextual dependencies, with a routing controller dynamically selecting experts based on real-time feature saliency. Extensive experiments on four benchmark datasets demonstrate that LGEST consistently outperforms state-of-the-art methods.

Jiawen Wen, Suixuan Qiu, Zihang Luo +2

Information Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, 511453, China · Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China · School of Electronic and Communication Engineering, Guangzhou University, Guangzhou University, Guangzhou, Guangzhou, 510006, China

Hyperspectral Image Classification Hybrid CNN-Transformer Architectures Expert Routing Multi-Scale Feature Fusion Mixture-of-Experts Models

Mar 22, 2026

B-jet Tagging Using a Hybrid Edge Convolution and Transformer Architecture

Jet flavor tagging plays an important role in precise Standard Model measurement enabling the extraction of mass dependence in jet-quark interaction and quark-gluon plasma (QGP) interactions. They also enable inferring the nature of particles produced in high-energy particle collisions that contain heavy quarks. The classification of bottom jets is vital for exploring new Physics scenarios in proton-proton collisions. In this research, we present a hybrid deep learning architecture that integrates edge convolutions with transformer self-attention mechanisms, into one single architecture called the Edge Convolution Transformer (ECT) model for bottom-quark jet tagging. ECT processes track-level features (impact parameters, momentum, and their significances) alongside jet-level observables (vertex information and kinematics) to achieve state-of-the-art performance. The study utilizes the ATLAS simulation dataset. We demonstrate that ECT achieves 0.9333 AUC for b-jet versus combined charm and light jet discrimination, surpassing ParticleNet (0.8904 AUC) and the pure transformer baseline (0.9216 AUC). The model maintains inference latency below 0.060 ms per jet on modern GPUs, meeting the stringent requirements for real-time event selection at the LHC. Our results demonstrate that hybrid architectures combining local and global features offer superior performance for challenging jet classification tasks. The proposed architecture achieves good results in b-jet tagging, particularly excelling in charm jet rejection (the most challenging task), while maintaining competitive light-jet discrimination comparable to pure transformer models.

Diego F. Vasquez Plaza, Vidya Manian

Department of Electrical and Computer Engineering, University of Puerto Rico Mayaguez, PR 00681-9000, USA

Jet Tagging High-Energy Physics Hybrid CNN-Transformer Architectures Hybrid Attention

Mar 19, 2026

Benchmarking CNN-based Models against Transformer-based Models for Abdominal Multi-Organ Segmentation on the RATIC Dataset

Accurate multi-organ segmentation in abdominal CT scans is essential for computer-aided diagnosis and treatment. While convolutional neural networks (CNNs) have long been the standard approach in medical image segmentation, transformer-based architectures have recently gained attention due to their ability to model long-range dependencies. In this study, we systematically benchmark the three hybrid transformer-based models UNETR, SwinUNETR, and UNETR++ against a strong CNN baseline, SegResNet, for volumetric multi-organ segmentation on the heterogeneous RATIC dataset. The dataset comprises 206 annotated CT scans from 23 institutions worldwide, covering five abdominal organs. All models were trained and evaluated under identical preprocessing and training conditions using the Dice Similarity Coefficient (DSC) as the primary metric. The results show that the CNN-based SegResNet achieves the highest overall performance, outperforming all hybrid transformer-based models across all organs. Among the transformer-based approaches, UNETR++ delivers the most competitive results, while UNETR demonstrates notably faster convergence with fewer training iterations. These findings suggest that, for small- to medium-sized heterogeneous datasets, well-optimized CNN architectures remain highly competitive and may outperform hybrid transformer-based designs.

Lukas Bayer, Sheethal Bhat, Andreas Maier

Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg

Medical Image Segmentation Hybrid CNN-Transformer Architectures Convolutional Neural Networks Medical Image Benchmarks Computed Tomography

Mar 12, 2026

UCAN: Unified Convolutional Attention Network for Expansive Receptive Fields in Lightweight Super-Resolution

Hybrid CNN-Transformer architectures achieve strong results in image super-resolution, but scaling attention windows or convolution kernels significantly increases computational cost, limiting deployment on resource-constrained devices. We present UCAN, a lightweight network that unifies convolution and attention to expand the effective receptive field efficiently. UCAN combines window-based spatial attention with a Hedgehog Attention mechanism to model both local texture and long-range dependencies, and introduces a distillation-based large-kernel module to preserve high-frequency structure without heavy computation. In addition, we employ cross-layer parameter sharing to further reduce complexity. On Manga109 (

4\times

), UCAN-L achieves 31.63 dB PSNR with only 48.4G MACs, surpassing recent lightweight models. On BSDS100, UCAN attains 27.79 dB, outperforming methods with significantly larger models. Extensive experiments show that UCAN achieves a superior trade-off between accuracy, efficiency, and scalability, making it well-suited for practical high-resolution image restoration.

Cao Thien Tan, Phan Thi Thu Trang, Do Nghiem Duc +3

Ho Chi Minh City Open University · AI Tech Lab, Ho Chi Minh City University of Technology · Code Mely AI Research Team +3

Image Super-Resolution Lightweight CNNs Hybrid CNN-Transformer Architectures Hybrid Attention

Mar 11, 2026

Combining Microscopy Data and Metadata for Reconstruction of Cellular Traction Forces Using a Hybrid Vision Transformer-U-Net

Traction force microscopy (TFM) is a widely used technique for quantifying the forces that cells exert on their surrounding extracellular matrix. Although deep learning methods have recently been applied to TFM data analysis, several challenges remain-particularly achieving reliable inference across multiple spatial scales and integrating additional contextual information such as cell type to improve accuracy. In this study, we propose ViT+UNet, a robust deep learning architecture that integrates a U-Net with a Vision Transformer. Our results demonstrate that this hybrid model outperforms both standalone U-Net and Vision Transformer architectures in predicting traction force fields. Furthermore, ViT+UNet exhibits superior generalization across diverse spatial scales and varying noise levels, enabling its application to TFM datasets obtained from different experimental setups and imaging systems. By appropriately structuring the input data, our approach also allows the inclusion of metadata, in our case cell-type information, to enhance prediction specificity and accuracy.

Yunfei Huang, Elena Van der Vorst, Alexander Richard +1

Fakultät Physik, Technische Universität Dortmund, Dortmund, 44227, Germany. · Faculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität München, Munich, 80752, Germany. · Department of Veterinary Sciences, Ludwig-Maximilians-Universität München, Munich, 80752, Germany.

Microscopy Image Analysis U-Net Hybrid CNN-Transformer Architectures

Mar 6, 2026

Remote Sensing Image Classification Using Deep Ensemble Learning

Remote sensing imagery plays a crucial role in many applications and requires accurate computerized classification techniques. Reliable classification is essential for transforming raw imagery into structured and usable information. While Convolutional Neural Networks (CNNs) are mostly used for image classification, they excel at local feature extraction, but struggle to capture global contextual information. Vision Transformers (ViTs) address this limitation through self attention mechanisms that model long-range dependencies. Integrating CNNs and ViTs, therefore, leads to better performance than standalone architectures. However, the use of additional CNN and ViT components does not lead to further performance improvement and instead introduces a bottleneck caused by redundant feature representations. In this research, we propose a fusion model that combines the strengths of CNNs and ViTs for remote sensing image classification. To overcome the performance bottleneck, the proposed approach trains four independent fusion models that integrate CNN and ViT backbones and combine their outputs at the final prediction stage through ensembling. The proposed method achieves accuracy rates of 98.10 percent, 94.46 percent, and 95.45 percent on the UC Merced, RSSCN7, and MSRSI datasets, respectively. These results outperform competing architectures and highlight the effectiveness of the proposed solution, particularly due to its efficient use of computational resources during training.

Niful Islam, Md. Rayhan Ahmed, Nur Mohammad Fahad +4

Department of Computer Science and Engineering, Oakland University, Michigan, USA · Irving K. Barber Faculty of Science, University of British Columbia, Kelowna, Canada · Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh +3

Remote Sensing Image Understanding Hybrid CNN-Transformer Architectures Deep Ensembles

Mar 3, 2026

Gated Differential Linear Attention: A Linear-Time Decoder for High-Fidelity Medical Segmentation

Medical image segmentation requires models that preserve fine anatomical boundaries while remaining practical for clinical deployment. Transformers capture long-range dependencies but incur quadratic attention cost, whereas CNNs are efficient but less effective at global reasoning. Linear attention offers

\mathcal{O}(N)

scaling, but often produces diffuse feature aggregation that weakens boundary-sensitive prediction. We introduce a gated differential linear-attention mixer for medical image segmentation. Its global path, Gated Differential Linear Attention (GDLA), performs differential subtraction between two kernelized attention branches over complementary query/key subspaces to suppress redundant responses, and employs a data-dependent gate for token refinement. A parallel local token-mixing branch with depthwise convolution strengthens neighborhood interactions for better refinement, and the two branches are fused while preserving

\mathcal{O}(N)

complexity. When instantiated in a pretrained Pyramid Vision Transformer (PVT)-based encoder--decoder model, \name achieves state-of-the-art results on the evaluated 2D medical segmentation benchmarks spanning CT, MRI, ultrasound, and dermoscopy, with a favorable accuracy--efficiency trade-off over closely related baselines. The code is publicly available at https://github.com/xmindflow/gdla.

Hongbo Zheng, Afshin Bozorgpour, Dorit Merhof +1

University of Illinois Urbana-Champaign · University of Regensburg

Medical Image Segmentation Linear Attention Gated Attention Boundary-Aware Segmentation Hybrid CNN-Transformer Architectures

Mar 2, 2026

MixerCSeg: An Efficient Mixer Architecture for Crack Segmentation via Decoupled Mamba Attention

Feature encoders play a key role in pixel-level crack segmentation by shaping the representation of fine textures and thin structures. Existing CNN-, Transformer-, and Mamba-based models each capture only part of the required spatial or structural information, leaving clear gaps in modeling complex crack patterns. To address this, we present MixerCSeg, a mixer architecture designed like a coordinated team of specialists, where CNN-like pathways focus on local textures, Transformer-style paths capture global dependencies, and Mamba-inspired flows model sequential context within a single encoder. At the core of MixerCSeg is the TransMixer, which explores Mamba's latent attention behavior while establishing dedicated pathways that naturally express both locality and global awareness. To further enhance structural fidelity, we introduce a spatial block processing strategy and a Direction-guided Edge Gated Convolution (DEGConv) that strengthens edge sensitivity under irregular crack geometries with minimal computational overhead. A Spatial Refinement Multi-Level Fusion (SRF) module is then employed to refine multi-scale details without increasing complexity. Extensive experiments on multiple crack segmentation benchmarks show that MixerCSeg achieves state-of-the-art performance with only 2.05 GFLOPs and 2.54 M parameters, demonstrating both efficiency and strong representational capability. The code is available at https://github.com/spiderforest/MixerCSeg.

Zilong Zhao, Zhengming Ding, Pei Niu +2

School of Qilu Transportation, Shandong University, China · Department of Computer Science, Tulane University, USA

Crack Segmentation Hybrid CNN-Transformer Architectures Boundary-Aware Segmentation

Feb 26, 2026

HARU-Net: Hybrid Attention Residual U-Net for Edge-Preserving Denoising in Cone-Beam Computed Tomography

Cone-beam computed tomography (CBCT) is widely used in dental and maxillofacial imaging, but low-dose acquisition introduces strong, spatially varying noise that degrades soft-tissue visibility and obscures fine anatomical structures. Classical denoising methods struggle to suppress noise in CBCT while preserving edges. Although deep learning-based approaches offer high-fidelity restoration, their use in CBCT denoising is limited by the scarcity of high-resolution CBCT data for supervised training. To address this research gap, we propose a novel Hybrid Attention Residual U-Net (HARU-Net) for high-quality denoising of CBCT data, trained on a cadaver dataset of human hemimandibles acquired using a high-resolution protocol of the 3D Accuitomo 170 (J. Morita, Kyoto, Japan) CBCT system. The novel contribution of this approach is the integration of three complementary architectural components: (i) a hybrid attention transformer block (HAB) embedded within each skip connection to selectively emphasize salient anatomical features, (ii) a residual hybrid attention transformer group (RHAG) at the bottleneck to strengthen global contextual modeling and long-range feature interactions, and (iii) residual learning convolutional blocks to facilitate deeper, more stable feature extraction throughout the network. HARU-Net consistently outperforms state-of-the-art (SOTA) methods including SwinIR and Uformer, achieving the highest PSNR (37.52 dB), highest SSIM (0.9557), and lowest GMSD (0.1084). This effective and clinically reliable CBCT denoising is achieved at a computational cost significantly lower than that of the SOTA methods, offering a practical advancement toward improving diagnostic quality in low-dose CBCT imaging.

Khuram Naveed, Ruben Pauwels

Department of Dentistry and Oral Health, Aarhus University, Aarhus, 8000, Denmark

Image Denoising LDCT Denoising Cone-Beam CT Hybrid CNN-Transformer Architectures

Feb 24, 2026

Le-DETR: Revisiting Real-Time Detection Transformer with Efficient Encoder Design

Real-time object detection is crucial for real-world applications as it requires high accuracy with low latency. While Detection Transformers (DETR) have demonstrated significant performance improvements, current real-time DETR models are challenging to reproduce from scratch due to excessive pre-training overheads on the backbone, constraining research advancements by hindering the exploration of novel backbone architectures. In this paper, we want to show that by using general good design, it is possible to have high performance with low pre-training cost. After a thorough study of the backbone architecture, we propose EfficientNAT at various scales, which incorporates modern efficient convolution and local attention mechanisms. Moreover, we re-design the hybrid encoder with local attention, significantly enhancing both performance and inference speed. Based on these advancements, we present Le-DETR (Low-cost and Efficient DEtection TRansformer), which achieves a new SOTA in real-time detection using only ImageNet1K and COCO2017 training datasets, saving about 80% images in pre-training stage compared with previous methods. We demonstrate that with well-designed, real-time DETR models can achieve strong performance without the need for complex and computationally expensive pretraining. Extensive experiments show that Le-DETR-M/L/X achieves 52.9/54.3/55.1 mAP on COCO Val2017 with 4.45/5.01/6.68 ms on an RTX4090. It surpasses YOLOv12-L/X by +0.6/-0.1 mAP while achieving similar speed and +20% speedup. Compared with DEIM-D-FINE, Le-DETR-M achieves +0.2 mAP with slightly faster inference, and surpasses DEIM-D-FINE-L by +0.4 mAP with only 0.4 ms additional latency. Code and weights will be open-sourced.

Jiannan Huang, Aditya Kane, Fengzhe Zhou +2

Beijing Jiaotong University

Real-Time Object Detection Efficient Object Detection Detection Transformer Efficient ViTs Hybrid CNN-Transformer Architectures

Feb 23, 2026

Token-UNet: A New Case for Transformers Integration in Efficient and Interpretable 3D UNets for Brain Imaging Segmentation

We present Token-UNet, adopting the TokenLearner and TokenFuser modules to encase Transformers into UNets. While Transformers have enabled global interactions among input elements in medical imaging, current computational challenges hinder their deployment on common hardware. Models like (Swin)UNETR adapt the UNet architecture by incorporating (Swin)Transformer encoders, which process tokens that each represent small subvolumes (

8^3

voxels) of the input. The Transformer attention mechanism scales quadratically with the number of tokens, which is tied to the cubic scaling of 3D input resolution. This work reconsiders the role of convolution and attention, introducing Token-UNets, a family of 3D segmentation models that can operate in constrained computational environments and time frames. To mitigate computational demands, our approach maintains the convolutional encoder of UNet-like models, and applies TokenLearner to 3D feature maps. This module pools a preset number of tokens from local and global structures. Our results show this tokenization effectively encodes task-relevant information, yielding naturally interpretable attention maps. The memory footprint, computation times at inference, and parameter counts of our heaviest model are reduced to 33%, 10%, and 35% of the SwinUNETR values, with better average performance (86.75%

\pm 0.19\%

Dice score for SwinUNETR vs our 87.21%

\pm 0.35\%

). This work opens the way to more efficient trainings in contexts with limited computational resources, such as 3D medical imaging. Easing model optimization, fine-tuning, and transfer-learning in limited hardware settings can accelerate and diversify the development of approaches, for the benefit of the research community.

Louis Fabrice Tshimanga, Andrea Zanola, Federico Del Pup +1

3D Medical Image Segmentation Hybrid CNN-Transformer Architectures Efficient ViTs Neuroimaging

Feb 21, 2026

NeXt2Former-CD: Efficient Remote Sensing Change Detection with Modern Vision Architectures

State Space Models (SSMs) have recently gained traction in remote sensing change detection (CD) for their favorable scaling properties. In this paper, we explore the potential of modern convolutional and attention-based architectures as a competitive alternative. We propose NeXt2Former-CD, an end-to-end framework that integrates a Siamese ConvNeXt encoder initialized with DINOv3 weights, a deformable attention-based temporal fusion module, and a Mask2Former decoder. This design is intended to better tolerate residual co-registration noise and small object-level spatial shifts, as well as semantic ambiguity in bi-temporal imagery. Experiments on LEVIR-CD, WHU-CD, and CDD datasets show that our method achieves the best results among the evaluated methods, improving over recent Mamba-based baselines in both F1 score and IoU. Furthermore, despite a larger parameter count, our model maintains inference latency comparable to SSM-based approaches, suggesting it is practical for high-resolution change detection tasks.

Yufan Wang, Sokratis Makrogiannis, Chandra Kambhamettu

University of South Florida · Delaware State University

Remote Sensing Change Detection Hybrid CNN-Transformer Architectures Convolutional Neural Networks

Feb 19, 2026

A Hybrid Federated Learning Based Ensemble Approach for Lung Disease Diagnosis Leveraging Fusion of SWIN Transformer and CNN

The significant advancements in computational power cre- ate a vast opportunity for using Artificial Intelligence in different ap- plications of healthcare and medical science. A Hybrid FL-Enabled Ensemble Approach For Lung Disease Diagnosis Leveraging a Combination of SWIN Transformer and CNN is the combination of cutting-edge technology of AI and Federated Learning. Since, medi- cal specialists and hospitals will have shared data space, based on that data, with the help of Artificial Intelligence and integration of federated learning, we can introduce a secure and distributed system for medical data processing and create an efficient and reliable system. The proposed hybrid model enables the detection of COVID-19 and Pneumonia based on x-ray reports. We will use advanced and the latest available tech- nology offered by Tensorflow and Keras along with Microsoft-developed Vision Transformer, that can help to fight against the pandemic that the world has to fight together as a united. We focused on using the latest available CNN models (DenseNet201, Inception V3, VGG 19) and the Transformer model SWIN Transformer in order to prepare our hy- brid model that can provide a reliable solution as a helping hand for the physician in the medical field. In this research, we will discuss how the Federated learning-based Hybrid AI model can improve the accuracy of disease diagnosis and severity prediction of a patient using the real-time continual learning approach and how the integration of federated learn- ing can ensure hybrid model security and keep the authenticity of the information.

Asif Hasan Chowdhury, Md. Fahim Islam, M Ragib Anjum Riad +3

BRAC University, Dhaka-1212, Bangladesh

Medical Diagnosis Chest X-Ray Classification Hybrid CNN-Transformer Architectures Ensemble Learning Federated Learning

Feb 18, 2026

RefineFormer3D: Efficient 3D Medical Image Segmentation via Adaptive Multi-Scale Transformer with Cross Attention Fusion

Accurate and computationally efficient 3D medical image segmentation remains a critical challenge in clinical workflows. Transformer-based architectures often demonstrate superior global contextual modeling but at the expense of excessive parameter counts and memory demands, restricting their clinical deployment. We propose RefineFormer3D, a lightweight hierarchical transformer architecture that balances segmentation accuracy and computational efficiency for volumetric medical imaging. The architecture integrates three key components: (i) GhostConv3D-based patch embedding for efficient feature extraction with minimal redundancy, (ii) MixFFN3D module with low-rank projections and depthwise convolutions for parameter-efficient feature extraction, and (iii) a cross-attention fusion decoder enabling adaptive multi-scale skip connection integration. RefineFormer3D contains only 2.94M parameters, substantially fewer than contemporary transformer-based methods. Extensive experiments on ACDC and BraTS benchmarks demonstrate that RefineFormer3D achieves 93.44% and 85.9% average Dice scores respectively, outperforming or matching state-of-the-art methods while requiring significantly fewer parameters. Furthermore, the model achieves fast inference (8.35 ms per volume on GPU) with low memory requirements, supporting deployment in resource-constrained clinical environments. These results establish RefineFormer3D as an effective and scalable solution for practical 3D medical image segmentation.

Kavyansh Tyagi, Vishwas Rathi, Puneet Goyal

National Institute of Technology Kurukshetra Department of Electronics and Communication Engineering · National Institute of Technology Kurukshetra Department of Computer Engineering · Indian Institute of Technology Ropar Department of Computer Science and Engineering

3D Medical Image Segmentation 3D ViTs Efficient ViTs Hybrid CNN-Transformer Architectures

Feb 18, 2026

UCTECG-Net: Uncertainty-aware Convolution Transformer ECG Network for Arrhythmia Detection

Deep learning has improved automated electrocardiogram (ECG) classification, but limited insight into prediction reliability hinders its use in safety-critical settings. This paper proposes UCTECG-Net, an uncertainty-aware hybrid architecture that combines one-dimensional convolutions and Transformer encoders to process raw ECG signals and their spectrograms jointly. Evaluated on the MIT-BIH Arrhythmia and PTB Diagnostic datasets, UCTECG-Net outperforms LSTM, CNN1D, and Transformer baselines in terms of accuracy, precision, recall and F1 score, achieving up to 98.58% accuracy on MIT-BIH and 99.14% on PTB. To assess predictive reliability, we integrate three uncertainty quantification methods (Monte Carlo Dropout, Deep Ensembles, and Ensemble Monte Carlo Dropout) into all models and analyze their behavior using an uncertainty-aware confusion matrix and derived metrics. The results show that UCTECG-Net, particularly with Ensemble or EMCD, provides more reliable and better-aligned uncertainty estimates than competing architectures, offering a stronger basis for risk-aware ECG decision support.

Hamzeh Asgharnezhad, Pegah Tabarisaadi, Abbas Khosravi +2

Deakin University, Australia . · School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD, Australia .

Electrocardiogram Classification Hybrid CNN-Transformer Architectures Uncertainty Quantification

Jan 11, 2026

Categorize Early, Integrate Late: Divergent Processing Strategies in Automatic Speech Recognition

In speech language modeling, two architectures dominate the frontier: the Transformer and the Conformer. However, it remains unknown whether their comparable performance stems from convergent processing strategies or distinct architectural inductive biases. We introduce Architectural Fingerprinting, a probing framework that isolates the effect of architecture on representation, and apply it to a controlled suite of 24 pre-trained encoders (39M-3.3B parameters). Our analysis reveals divergent hierarchies: Conformers implement a "Categorize Early" strategy, resolving phoneme categories 29% earlier in depth and speaker gender by 16% depth. In contrast, Transformers "Integrate Late," deferring phoneme, accent, and duration encoding to deep layers (49-57%). These fingerprints suggest design heuristics: Conformers' front-loaded categorization may benefit low-latency streaming, while Transformers' deep integration may favor tasks requiring rich context and cross-utterance normalization.

Nathan Roll, Pranav Bhalerao, Martijn Bartelds +7

Stanford University

Audio Representation Learning Hybrid CNN-Transformer Architectures Transformer Automatic Speech Recognition Representation Probing

Dec 26, 2025

DuaDeep-SeqAffinity: Dual-Branch Deep Learning for Tri-Stream Sequence-Based Antibody--Antigen Affinity Prediction

DuaDeep-SeqAffinity is a sequence-only deep learning framework that predicts antibody--antigen binding affinity directly from primary amino acid sequences, avoiding the cost and scarcity of resolved three-dimensional structures. The antigen and the antibody heavy and light chains are processed as three independent streams, each embedded with a frozen ESM-2 protein language model and passed through parallel Transformer and convolutional neural network (CNN) branches before late fusion, a decoupled design intended to preserve local complementarity-determining region (CDR) signal that monolithic encoders can dilute. On a sequence-disjoint split of the AbRank benchmark, the model achieves a Pearson correlation of 0.683, an R^2 of 0.460, and a pairwise ranking AUC of 0.895, significantly outperforming single-branch ablations (paired t-test, p < 0.05). Attention-map and gradient-based saliency analyses further show that the model preferentially attends to CDR loops and candidate epitope residues, supporting its use as a scalable, structure-free tool for high-throughput antibody screening.

Aicha Boutorh, Soumia Bouyahiaoui, Manel Kara Laouar +3

National School of Artificial Intelligence (ENSIA) Sidi Abdallah Campus, Algiers, Algeria · Lamine Debaghine University Hospital Center, CHU Bab El Oued, Algiers, Algeria

Hybrid CNN-Transformer Architectures Molecular Property Prediction

Nov 19, 2025

BrainRotViT: Transformer-ResNet Hybrid for Explainable Modeling of Brain Aging from 3D sMRI

Accurate brain-age estimation from structural MRI provides a valuable biomarker for studying healthy aging and neurodegenerative disease. We propose BrainRotViT (Brain ResNet over trained Vision Transformer), a two-stage, slice-based (2.5D) framework that approximates volumetric context while combining the strengths of CNNs and transformers. A Vision Transformer is pretrained on an auxiliary age-sex composite classification task and subsequently frozen. Its per-slice embeddings are stacked into a 160 x 768 feature matrix and processed as a pseudo-image by a lightweight 2D residual CNN regressor, with subject sex incorporated at the final prediction layer. Trained and validated on 5,108 samples from 11 multi-site datasets and tested on four independent held-out cohorts comprising 948 samples, BrainRotViT achieves a validation mean absolute error (MAE) of 3.43 years (Pearson r = 0.98,

R^2

= 0.96, Spearman

ρ

= 0.97) and cross-cohort MAEs of 4.79-5.35 years, outperforming retrained 3D-ResNet, SFCN, 3D-ViT, Global-Local Transformer, Triamese-ViT, and TSAN baselines. After age-bias correction and covariate adjustment, brain-age gap is significantly associated with Alzheimer's disease, mild cognitive impairment, and autism spectrum disorder. An interpretability pipeline combining ViT attention with guided backpropagation, saliency sanity checks, and AAL3-based anatomical localization identifies aging-related regions including the occipital lobe, cerebellum, precentral and postcentral gyri, temporal lobes, and medial superior frontal gyrus. BrainRotViT thus provides an efficient and interpretable bridge between CNN and transformer paradigms, demonstrating cross-cohort generalization while yielding biologically meaningful insights into brain aging.

Wasif Jalal, Md Nafiu Rahman, Atif Hasan Rahman +1

Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology

Brain Age Prediction Brain MRI Hybrid CNN-Transformer Architectures Explainable Medical Image Analysis

Nov 18, 2025

Unifying Convolution and Attention via Convolutional Nearest Neighbors

Convolutional Neural Networks and Vision Transformers are the two dominant architectural families in computer vision, defined by spatially local convolution and global self-attention respectively. Despite their apparent differences, we show that both operations are special cases of a single

k

-nearest neighbor aggregation framework: convolution selects neighbors by spatial proximity while attention selects by feature similarity, placing them at two ends of a shared operational spectrum. We introduce Convolutional Nearest Neighbors (ConvNN), a unified framework that exactly recovers standard and depthwise convolution, self-attention, and sparse attention variants including KVT-attention as special cases, and exposes the design space of neighbor-selection strategies between them through configurable similarity functions, positional encodings, and aggregation kernels. We validate ConvNN on ImageNet-1K classification across two complementary architectures: a hybrid branching layer in ResNet-50 that combines local and global feature learning, improving top-1 accuracy by 3.0% over the ResNet-50 baseline, and ConvNN-attention in ViT-Base that achieves 81.64% top-1 accuracy, surpassing standard multi-head self-attention by 0.7%. Together, these results demonstrate that ConvNN provides a principled foundation for designing operations that bridge convolutional and attention-based computation.

Mingi Kang, Jeová Farias Sales Rocha Neto

Bowdoin College

Hybrid CNN-Transformer Architectures Self-Attention Vision Transformer Convolutional Neural Networks Attention Mechanisms k-Nearest Neighbors

Sep 26, 2025

Johnson-Lindenstrauss Lemma Guided Network for Efficient 3D Medical Segmentation

Lightweight 3D medical image segmentation remains constrained by a fundamental *

efficiency / robustness conflict''*, particularly when processing complex anatomical structures and heterogeneous modalities. In this paper, we study how to redesign the framework based on the characteristics of high-dimensional 3D images, and explore data synergy to overcome the fragile representation of lightweight methods. Our approach, VeloxSeg, begins with a deployable and extensible dual-stream CNN-Transformer architecture composed of Paired Window Attention (PWA) and Johnson-Lindenstrauss lemma-guided convolution (JLC). For each 3D image, we invoke a

glance-and-focus'' principle, where PWA rapidly retrieves multi-scale information, and JLC ensures robust local feature extraction with minimal parameters, significantly enhancing the model's ability to operate with low computational budget. Followed by an extension of the dual-stream architecture that incorporates modal interaction into the multi-scale image-retrieval process, VeloxSeg efficiently models heterogeneous modalities. Finally, Spatially Decoupled Knowledge Transfer (SDKT) via Gram matrices injects the texture prior extracted by a self-supervised network into the segmentation network, yielding stronger representations than baselines at no extra inference cost. Experimental results on multimodal benchmarks show that VeloxSeg achieves a 26% Dice improvement, alongside increasing GPU throughput by 11

\times

, CPU by 48

\times

, and reducing training peak GPU memory usage by

1/20

, inference by

1/24

. Code is available at https://github.com/JinPLu/VeloxSeg.

Jinpeng Lu, Linghan Cai, Yinda Chen +4

University of Science and Technology of China · Dresden University of Technology · Harbin Institute of Technology, Shenzhen

Cross-Modality Medical Image Segmentation Image Segmentation Hybrid CNN-Transformer Architectures 3D Medical Image Segmentation

Jul 16, 2025

HyDRA: A Hybrid Dual-Mode Network for Closed- and Open-Set RFFI with Optimized VMD

Device recognition is vital for security in wireless communication systems, particularly for applications like access control. Radio Frequency Fingerprint Identification (RFFI) offers a non-cryptographic solution by exploiting hardware-induced signal distortions. This paper proposes HyDRA, a Hybrid Dual-mode RF Architecture that integrates an optimized Variational Mode Decomposition (VMD) with a novel architecture based on the fusion of Convolutional Neural Networks (CNNs), Transformers, and Mamba components, designed to support both closed-set and open-set classification tasks. The optimized VMD enhances preprocessing efficiency and classification accuracy by fixing center frequencies and using closed-form solutions. HyDRA employs the Transformer Dynamic Sequence Encoder (TDSE) for global dependency modeling and the Mamba Linear Flow Encoder (MLFE) for linear-complexity processing, adapting to varying conditions. Evaluation on public datasets demonstrates state-of-the-art (SOTA) accuracy in closed-set scenarios and robust performance in our proposed open-set classification method, effectively identifying unauthorized devices. Deployed on NVIDIA Jetson Xavier NX, HyDRA achieves millisecond-level inference speed with low power consumption, providing a practical solution for real-time wireless authentication in real-world environments. The source code is published on https://github.com/Crazy-Bull/HyDRA.

Hanwen Liu, Yuhe Huang, Yifeng Gong +2

School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen, 518107, China

Hybrid CNN-Transformer Architectures RF Fingerprint Identification Open-Set Recognition

Dec 3, 2024

Cascaded Multi-Scale Attention for Enhanced Multi-Scale Feature Extraction and Interaction with Low-Resolution Images

In real-world applications of image recognition tasks, such as human pose estimation, cameras often capture objects, like human bodies, at low resolutions. This scenario poses a challenge in extracting and leveraging multi-scale features, which is often essential for precise inference. To address this challenge, we propose a new attention mechanism, named cascaded multi-scale attention (CMSA), tailored for use in CNN-ViT hybrid architectures, to handle low-resolution inputs effectively. The design of CMSA enables the extraction and seamless integration of features across various scales without necessitating the downsampling of the input image or feature maps. This is achieved through a novel combination of grouped multi-head self-attention mechanisms with window-based local attention and cascaded fusion of multi-scale features over different scales. This architecture allows for the effective handling of features across different scales, enhancing the model's ability to perform tasks such as human pose estimation, head pose estimation, and more with low-resolution images. Our experimental results show that the proposed method outperforms existing state-of-the-art methods in these areas with fewer parameters, showcasing its potential for broad application in real-world scenarios where capturing high-resolution images is not feasible. Code is available at https://github.com/xyongLu/CMSA.

Xiangyong Lu, Masanori Suganuma, Takayuki Okatani

Graduate School of Information Sciences, Tohoku University. · RIKEN Center for AIP

Hybrid CNN-Transformer Architectures Object Pose Estimation Multi-Scale Feature Fusion Human Pose Estimation Multi-Scale Attention

May 12, 2024

Explainable Convolutional Neural Networks for Retinal Fundus Classification and Cutting-Edge Segmentation Models for Retinal Blood Vessels from Fundus Images

Early detection of vision-threatening conditions such as diabetic retinopathy, glaucoma, and age-related macular degeneration depends on retinal fundus image analysis, but manual assessment is slow and expert-dependent. Automated convolutional neural networks classify fundus images accurately yet act as black boxes, and existing retinal vessel segmentation methods lose discriminative power under pathology and seldom exploit attention or transformer backbones. Using the FIVES and DRIVE fundus datasets, we develop a two-pipeline framework that pairs four-class disease classification with attention- and transformer-based vessel segmentation, organised in three stages: (1) FIVES images are augmented by rotation and horizontal and vertical flips and used to fine-tune eight ImageNet-pretrained CNNs: ResNet101, DenseNet169, Xception, InceptionV3, DenseNet121, InceptionResNetV2, ResNet50, and EfficientNetB0. (2) Five gradient-based explanation methods, Grad-CAM, Grad-CAM++, Score-CAM, Faster Score-CAM, and Layer-CAM, are computed on the final convolutional block of each classifier and compared qualitatively across architectures. (3) Ten U-Net variants are benchmarked for vessel segmentation: TransUNet (hybrid CNN--Transformer encoder) and Attention U-Net (gated skip connections), evaluated with ResNet50V2, ResNet101V2, and ResNet152V2 backbones, along with additional Attention U-Net configurations using DenseNet backbones, and the fully transformer-based Swin-UNet. ResNet101 gives the highest classification accuracy: 94.17% (F1 0.942)

>

88.33% for EfficientNetB0. For segmentation, the architecture ranking is consistent on both datasets: Attention U-Net

>

TransUNet

>

Swin-UNet. The strongest configuration is Attention U-Net with a ResNet101V2 backbone: FIVES IoU 0.722, Dice 0.838; DRIVE IoU 0.648, Dice 0.787, lifting DRIVE IoU 60.80

\rightarrow

64.83 over a prior custom U-Net.

Fatema Tuj Johora Faria, Mukaffi Bin Moin, Pronay Debnath +2

aDepartment of Computer Science and Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh

Explainable Medical Image Analysis Retinal Vessel Segmentation Convolutional Neural Networks Image Segmentation Hybrid CNN-Transformer Architectures U-Net+5