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Works154 from public data
- Lednet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation411
This paper presents LEDNet, which employs an asymmetric encoder-decoder architecture for the task of real-time semantic segmentation, which adopts a ResNet as backbone network to greatly reduce computation cost while maintaining higher segmentation accuracy.
- Lightweight Real-Time Semantic Segmentation Network With Efficient Transformer and CNN185
This work proposes a lightweight real-time semantic segmentation network called LETNet that combines a U-shaped CNN with Transformer effectively in a capsule embedding style to compensate for respective deficiencies.
- CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution164
This work proposes an efficient CNN-Transformer Cooperation Network (CTCNet) for face super-resolution tasks, which uses the multi-scale connected encoder-decoder architecture as the backbone and devise a novel Local-Global Feature Cooperation Module (LGCM) to promote the consistency of local facial detail and global facial structure restoration simultaneously.
- FBSNet: A Fast Bilateral Symmetrical Network for Real-Time Semantic Segmentation154
This article proposes a Fast Bilateral Symmetrical Network (FBSNet), a symmetrical encoder-decoder structure with two branches, semantic information branch and spatial detail branch that can strike a good balance between accuracy and efficiency.
- MSCFNet: A Lightweight Network With Multi-Scale Context Fusion for Real-Time Semantic Segmentation146
A novel lightweight network using a multi-scale context fusion (MSCFNet) scheme, which explores an asymmetric encoder-decoder architecture to alleviate the trade-off between accuracy, inference speed, and model size.
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- Multi-scale deep context convolutional neural networks for semantic segmentation132
This paper designs a multi-scale deep context convolutional network (MDCCNet), which combines the feature maps from different levels of network in a holistic manner for semantic segmentation and demonstrates that the method outperforms or is comparable to state-of-the-art methods on PASCAL VOC 2012 and SIFTFlow semantic segmentations datasets.
- Feature Distillation Interaction Weighting Network for Lightweight Image Super-resolution122
This work proposes a lightweight yet efficient Feature Distillation Interaction Weighted Network (FDIWN), which utilizes a series of specially designed Feature Shuffle Weighted Groups as the backbone, and several novel mutual Wide-residual Distillation interaction Blocks form an FSWG.
- Chinese Image Captioning via Fuzzy Attention-based DenseNet-BiLSTM121
A fuzzy attention-based DenseNet-BiLSTM Chinese image captioning method that improves the problem of correspondence between image features and contextual information and achieves higher scores in objective quantitative evaluation indicators.
- Learning robust and discriminative low-rank representations for face recognition with occlusion114
This paper proposes a novel method by exploiting the low-rankness of both the data representation and each occlusion-induced error image simultaneously, by which the global structure of data together with the error images can be well captured.
- Adaptive deformable convolutional network94
This paper reformulates the deformable convolution and RoIpooling by reconsidering spatial-wise attention, channel-wise Attention and spatial-channel interdependency, to improve the single convolution’s ability to focus on pertinent image contents.
- Cross-Receptive Focused Inference Network for Lightweight Image Super-Resolution91
A novel Cross-receptive Field Guided Transformer is designed to enable the selection of contextual information required for reconstruction by using a modulated convolutional kernel that understands the current semantic information and exploits the information interaction within different self-attention.
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- A Systematic Survey of Deep Learning-Based Single-Image Super-Resolution79
This survey gives an overview of DL-based SISR methods and group them according to their design targets and presents the reconstruction results of some classic SISR methods to intuitively know their performance.
- Regional deep learning model for visual tracking78
A regional deep learning tracker that observes the target by multiple sub-regions and each region is observed by a deep learning model, and devise a stable factor, modeled as a hidden variable of the Factorial Hidden Markov Model, to characterize the stability of these sub-models.
- Constructing multilayer locality-constrained matrix regression framework for noise robust face super-resolution58
An efficient multilayer locality-constrained matrix regression (MLCMR) framework to learn the representation of the input LR patch and meanwhile preserve the manifold of the original HR space and aims at reducing the inconsistency gap between different manifolds.
- Hierarchical Deep CNN Feature Set-Based Representation Learning for Robust Cross-Resolution Face Recognition57
A feature set-based representation learning (FSRL) scheme to collaboratively represent the hierarchical features for more accurate recognition and the superiority of the presented algorithm to the other competitive CRFR approaches is verified.
- Discriminative histograms of local dominant orientation (D-HLDO) for biometric image feature extraction53
This paper presents a simple and robust method, namely discriminative histograms of local dominant orientation (D-HLDO), for biometric image feature extraction, applied to face, finger-knuckle-print and Palm biometrics and is examined using the AR, CMU PIE and FRGCv2.0 face image databases.
- Integration of multiple orientation and texture information for finger-knuckle-print verification51
A novel multiple orientation and texture information integration scheme is proposed in this paper that not only considers more orientations, but also introduces a multilevel image thresholding scheme to perform orientation coding on each Gabor filtering response.
- Cross-resolution face recognition with pose variations via multilayer locality-constrained structural orthogonal procrustes regression48
The proposed MLCSOPR not only learns the pose-robust discriminative representation features to reduce the resolution gap between the LR image space and the high-resolution (HR) one but also strengthens the consistency between theLR and HR image space.
- Efficient Image Super-Resolution With Feature Interaction Weighted Hybrid Network46
This work proposes a Feature Interaction Weighted Hybrid Network (FIWHN), which comprises a series of Wide-residual Distillation Interaction Block (WDIB) as the backbone, and incorporates a Transformer and explores a novel architecture to combine CNN and Transformer.
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- HAFormer: Unleashing the Power of Hierarchy-Aware Features for Lightweight Semantic Segmentation41
HAFormer is introduced, a model that combines the hierarchical features extraction ability of CNNs with the global dependency modeling capability of Transformers to tackle lightweight semantic segmentation challenges and achieves high performance with minimal computational overhead and compact model size.
- Collaborative representation based local discriminant projection for feature extraction41
Experimental results on ORL, AR and CMU PIE face databases validate the superiority of CRLDP over other state-of-the-art algorithms.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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