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- Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining926
A novel deep network architecture based on deep convolutional and recurrent neural networks for single image deraining based on contextual information is proposed and outperforms the state-of-the-art approaches under all evaluation metrics.
- Expectation-Maximization Attention Networks for Semantic Segmentation700
This paper forms the attention mechanism into an expectation-maximization manner and iteratively estimate a much more compact set of bases upon which the attention maps are computed, which is robust to the variance of input and is also friendly in memory and computation.
- MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation457
A Multi-Hypothesis Transformer (MHFormer) that learns spatio-temporal representations of multiple plausible pose hypotheses and achieves state-of-the-art results on two challenging datasets: Human3.6M and MPI-INF-3DHP.
- TDD‐net: a tiny defect detection network for printed circuit boards440
In this method, the inherent multi-scale and pyramidal hierarchies of deep convolutional networks are exploited to construct feature pyramids and the proposed method has better portability and can achieve 98.90% mAP, which outperforms the state-of-arts.
- Multi-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition390
A multi-scale spatial temporal graph convolutional network (MST-GCN), which stacks multiple blocks to learn effective motion representations for action recognition, achieves remarkable performance on three challenging benchmark datasets.
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- Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation348
This paper introduces a novel approach for monocular depth estimation which is competitive with previous methods on the KITTI benchmark and outperforms the state of the art on the NYU Depth V2 dataset.
- Exploiting Temporal Contexts With Strided Transformer for 3D Human Pose Estimation314
An improved Transformer-based architecture, called Strided Transformer, which simply and effectively lifts a long sequence of 2D joint locations to a single 3D pose, and achieves state-of-the-art results with fewer parameters.
- AttentionGAN: Unpaired Image-to-Image Translation Using Attention-Guided Generative Adversarial Networks292
A new Attention-Guided Generative Adversarial Networks (AttentionGAN) is proposed for the unpaired image-to-image translation task which can identify the most discriminative semantic objects and minimize changes of unwanted parts for semantic manipulation problems without using extra data and models.
- Violence detection using Oriented VIolent Flows282
A novel feature extraction method named Oriented VIolent Flows (OViF), which takes full advantage of the motion magnitude change information in statistical motion orientations, is proposed for practical violence detection in videos.
- AO2-DETR: Arbitrary-Oriented Object Detection Transformer278
This paper proposes an Arbitrary-Oriented Object DEtection TRansformer framework, termed AO2-DETR, which comprises three dedicated components and proposes an oriented proposal generation mechanism to explicitly generate oriented proposals, which provides better positional priors for pooling features to modulate the cross-attention in the transformer decoder.
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- A Survey on 3D Skeleton-Based Action Recognition Using Learning Method232
This research represents the first comprehensive discussion of deep learning-based action recognition using 3D skeleton data and provides a comprehensive introduction to mainstream action recognition techniques based on 4 fundamental deep architectures, i.e., recurrent neural networks, convolutional neural networks, graph convolutional network, and Transformers.
- Spatial Pyramid Based Graph Reasoning for Semantic Segmentation193
This paper applies graph convolution into the semantic segmentation task and proposes an improved Laplacian, which gets rid of projecting and re-projecting processes and makes spatial pyramid possible to explore multiple long-range contextual patterns from different scales.
- Data Uncertainty in Face Recognition181
This paper reduces the uncertainty of the face representation by synthesizing the virtual training samples and devise a representation approach based on the selected useful training samples to perform face recognition that can not only obtain a high face recognition accuracy, but also has a lower computational complexity than the other state-of-the-art approaches.
- Using the original and ‘symmetrical face’ training samples to perform representation based two-step face recognition163
This paper proposes to exploit the symmetry of the face to generate new samples and devise a representation based method to perform face recognition that outperforms state-of-the-art face recognition methods including the sparse representation classification (SRC), linear regression classification (LRC), collaborative representation (CR) and two-phase test sample sparse representation (TPTSSR).
- A gated cross-domain collaborative network for underwater object detection158
A Gated Cross-domain Collaborative Network (GCC-Net) is proposed to address the challenges of poor visibility and low contrast in underwater environments, which comprises three dedicated components.
- HRIPCB: a challenging dataset for PCB defects detection and classification145
Unlike conventional approaches that require pixel-by-pixel processing, the RBCNN method proposed in this study firstly locates the defects and then classifies them by deep neural networks, which shows superior performance on the dataset.
- Coarse to fine K nearest neighbor classifier142
The experimental results show that CFKNNC can classify much more accurately than CKNNC and various improvements to CKnnC such as the nearest feature line (NFL) classifier, the nearest features space (NFS) classifiers, nearest neighbor line classifier (NNLC) and center-based nearest neighbor classifier(CBNNC).
- Fast and robust dynamic hand gesture recognition via key frames extraction and feature fusion137
This work combines image entropy and density clustering to exploit the key frames from hand gesture video for further feature extraction, which can improve the efficiency of recognition.
- Online growing neural gas for anomaly detection in changing surveillance scenes118
A neural network based model called online Growing Neural Gas (online GNG) to perform an unsupervised learning to perform anomaly detection and effectively reduces the false alarms and leak detections caused by model aging which frequently happens in changing surveillance scenes.
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- When Dictionary Learning Meets Deep Learning: Deep Dictionary Learning and Coding Network for Image Recognition With Limited Data100
A new deep dictionary learning and coding network (DDLCN) for image-recognition tasks with limited data is presented and empirical results show that DDLCN achieves competitive results compared with state-of-the-art methods when the training data are limited.
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- Subcycle Dynamics of Coulomb Asymmetry in Strong Elliptical Laser Fields92
The theory quantifies the roles of the ionic potential and initial transverse momentum on Coulomb asymmetry, proving that the small lobes of asymmetry are induced by direct ionization and the strong asymmetry is induced by multiple forward scattering in the Ionic potential.
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 2026-10-13. 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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