Image Classification via Random Dilated Convolution with Multi-Branch Feature Extraction and Context Excitation

• URL: http://arxiv.org/abs/2604.25188v1
• Date: Tue, 28 Apr 2026 03:49:35 GMT
• Title: Image Classification via Random Dilated Convolution with Multi-Branch Feature Extraction and Context Excitation
• Abstract summary: RDCNet is a novel architecture built upon ResNet-34 that integrates three synergistic innovations to address these limitations.<n>RDCNet consistently achieves state-of-the-art classification accuracy, outperforming the second-best competing methods by margins of 0.02%, 1.12%, 0.18%, 4.73%, respectively.
• Score: 3.9240527145303123
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Image classification remains a fundamental yet challenging task in computer vision, particularly when fine-grained feature extraction and background noise suppression are required simultaneously. Conventional convolutional neural networks, despite their remarkable success in hierarchical feature learning, often struggle with capturing multi-scale contextual information and are susceptible to overfitting when confronted with noisy or irrelevant image regions. In this paper, we propose RDCNet (Image Classification Network with Random Dilated Convolution), a novel architecture built upon ResNet-34 that integrates three synergistic innovations to address these limitations: (1) a Multi-Branch Random Dilated Convolution (MRDC) module that employs parallel branches with varying dilation rates combined with a stochastic masking mechanism to capture fine-grained features across multiple scales while enhancing robustness against noise and overfitting; (2) a Fine-Grained Feature Enhancement (FGFE) module embedded within MRDC that bridges global contextual information with local feature representations through adaptive pooling and bilinear interpolation, thereby amplifying sensitivity to subtle visual patterns; and (3) a Context Excitation (CE) module that leverages softmax-based spatial attention and channel recalibration to dynamically emphasize task-relevant features while suppressing background interference. Extensive experiments conducted on five benchmark datasets -- CIFAR-10, CIFAR-100, SVHN, Imagenette, and Imagewoof -- demonstrate that RDCNet consistently achieves state-of-the-art classification accuracy, outperforming the second-best competing methods by margins of 0.02\%, 1.12\%, 0.18\%, 4.73\%, and 3.56\%, respectively, thereby validating the effectiveness and generalizability of the proposed approach across diverse visual recognition scenarios.

Related papers

• Graph-Based Uncertainty Modeling and Multimodal Fusion for Salient Object Detection [12.743278093269325]
  We propose a dynamic uncertainty propagation and multimodal collaborative reasoning network (DUP-MCRNet)<n>DUGC is designed to propagate uncertainty between layers through a sparse graph constructed based on spatial semantic distance.<n>MCF uses learnable modality gating weights to weightedly fuse the attention maps of RGB, depth, and edge features.
  arXiv  Detail & Related papers  (2025-08-28T04:31:48Z)
• Multi-View Learning with Context-Guided Receptance for Image Denoising [18.175992709188026]
  Image denoising is essential in low-level vision applications such as photography and automated driving.<n>Existing methods struggle with distinguishing complex noise patterns in real-world scenes and consume significant computational resources.<n>In this work, a Context-guided Receptance Weighted Key-Value (M) model is proposed, combining enhanced multi-view feature integration with efficient sequence modeling.<n>The model is validated on multiple real-world image denoising datasets, outperforming the existing state-of-the-art methods quantitatively and reducing inference time up to 40%.
  arXiv  Detail & Related papers  (2025-05-05T14:57:43Z)
• CFMD: Dynamic Cross-layer Feature Fusion for Salient Object Detection [7.262250906929891]
  Cross-layer feature pyramid networks (CFPNs) have achieved notable progress in multi-scale feature fusion and boundary detail preservation for salient object detection.<n>To address these challenges, we propose CFMD, a novel cross-layer feature pyramid network that introduces two key innovations.<n>First, we design a context-aware feature aggregation module (CFLMA), which incorporates the state-of-the-art Mamba architecture to construct a dynamic weight distribution mechanism.<n>Second, we introduce an adaptive dynamic upsampling unit (CFLMD) that preserves spatial details during resolution recovery.
  arXiv  Detail & Related papers  (2025-04-02T03:22:36Z)
• FUSE: Label-Free Image-Event Joint Monocular Depth Estimation via Frequency-Decoupled Alignment and Degradation-Robust Fusion [92.4205087439928]
  Image-event joint depth estimation methods leverage complementary modalities for robust perception, yet face challenges in generalizability.<n>We propose the Self-supervised Transfer (PST) and the FrequencyDe-coupled Fusion module (FreDF)<n>PST establishes cross-modal knowledge transfer through latent space alignment with image foundation models, effectively mitigating data scarcity.<n>FreDF explicitly decouples high-frequency edge features from low-frequency structural components, resolving modality-specific frequency mismatches.<n>This combined approach enables FUSE to construct a universal image-event that only requires lightweight decoder adaptation for target datasets.
  arXiv  Detail & Related papers  (2025-03-25T15:04:53Z)
• Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection [70.84835546732738]
  RGB-Thermal Salient Object Detection aims to pinpoint prominent objects within aligned pairs of visible and thermal infrared images.<n>Traditional encoder-decoder architectures may not have adequately considered the robustness against noise originating from defective modalities.<n>We propose the ConTriNet, a robust Confluent Triple-Flow Network employing a Divide-and-Conquer strategy.
  arXiv  Detail & Related papers  (2024-12-02T14:44:39Z)
• Double-Shot 3D Shape Measurement with a Dual-Branch Network for Structured Light Projection Profilometry [14.749887303860717]
  We propose a dual-branch Convolutional Neural Network (CNN)-Transformer network (PDCNet) to process different structured light (SL) modalities.<n>Within PDCNet, a Transformer branch is used to capture global perception in the fringe images, while a CNN branch is designed to collect local details in the speckle images.<n>Our method can reduce fringe order ambiguity while producing high-accuracy results on self-made datasets.
  arXiv  Detail & Related papers  (2024-07-19T10:49:26Z)
• DCNN: Dual Cross-current Neural Networks Realized Using An Interactive Deep Learning Discriminator for Fine-grained Objects [48.65846477275723]
  This study proposes novel dual-current neural networks (DCNN) to improve the accuracy of fine-grained image classification. The main novel design features for constructing a weakly supervised learning backbone model DCNN include (a) extracting heterogeneous data, (b) keeping the feature map resolution unchanged, (c) expanding the receptive field, and (d) fusing global representations and local features.
  arXiv  Detail & Related papers  (2024-05-07T07:51:28Z)
• Fully Differentiable Correlation-driven 2D/3D Registration for X-ray to CT Image Fusion [3.868072865207522]
  Image-based rigid 2D/3D registration is a critical technique for fluoroscopic guided surgical interventions. We propose a novel fully differentiable correlation-driven network using a dual-branch CNN-transformer encoder. A correlation-driven loss is proposed for low-frequency feature and high-frequency feature decomposition based on embedded information.
  arXiv  Detail & Related papers  (2024-02-04T14:12:51Z)
• Rotated Multi-Scale Interaction Network for Referring Remote Sensing Image Segmentation [63.15257949821558]
  Referring Remote Sensing Image (RRSIS) is a new challenge that combines computer vision and natural language processing. Traditional Referring Image (RIS) approaches have been impeded by the complex spatial scales and orientations found in aerial imagery. We introduce the Rotated Multi-Scale Interaction Network (RMSIN), an innovative approach designed for the unique demands of RRSIS.
  arXiv  Detail & Related papers  (2023-12-19T08:14:14Z)
• Connecting Multi-modal Contrastive Representations [50.26161419616139]
  Multi-modal Contrastive Representation learning aims to encode different modalities into a semantically shared space. This paper proposes a novel training-efficient method for learning MCR without paired data called Connecting Multi-modal Contrastive Representations (C-MCR) C-MCR achieves audio-visual state-of-the-art performance on audio-image retrieval, audio-visual source localization, and counterfactual audio-image recognition tasks.
  arXiv  Detail & Related papers  (2023-05-22T09:44:39Z)
• Searching Multi-Rate and Multi-Modal Temporal Enhanced Networks for Gesture Recognition [89.0152015268929]
  We propose the first neural architecture search (NAS)-based method for RGB-D gesture recognition. The proposed method includes two key components: 1) enhanced temporal representation via the 3D Central Difference Convolution (3D-CDC) family, and optimized backbones for multi-modal-rate branches and lateral connections. The resultant multi-rate network provides a new perspective to understand the relationship between RGB and depth modalities and their temporal dynamics.
  arXiv  Detail & Related papers  (2020-08-21T10:45:09Z)

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.