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Claim this profileWorks393 from public data
- 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.
- Achieving domain generalization for underwater object detection by domain mixup and contrastive learning91
This paper proposes a domain generalization framework (named DMC) that learns how to generalize across domains from Domain Mixup and Contrastive Learning, and demonstrates that the proposed method is able to learn domain-invariant representations, and outperforms otherdomain generalization methods.
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- CNN‐based reference comparison method for classifying bare PCB defects75
A traditional classification algorithm based on digital image processing was attempted, and a defect classification algorithms based on convolutional neural network was proposed, which can achieve a fairly high classification accuracy (95.7%), which is much higher than the traditional method, and the new method has stronger stability than thetraditional one.
- 3D action recognition using multi-temporal depth motion maps and fisher vector75
Extensive experiments on the public MSRAction3D, MSRGesture3D and DHA datasets show that the proposed method outperforms state-of-the-art approaches for depth-based action recognition.
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- Hourglass Tokenizer for Efficient Transformer-Based 3D Human Pose Estimation67
This paper proposes a token pruning cluster (TPC) that dynamically selects a few representative tokens with high semantic diversity while eliminating the redundancy of video frames and develops a token recovering attention (TRA) to restore the detailed spatio-temporal information based on the selected tokens, thereby expanding the network output to the original full-length temporal resolution for fast inference.
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- Locality and similarity preserving embedding for feature selection66
A novel unsupervised FS method, i.e., locality and similarity preserving embedding (LSPE) for feature selection, where the nearest neighbor graph is firstly constructed to preserve the locality structure of data points, and then this locality structure is mapped to the reconstruction coefficients such that the similarity among these data points is preserved.
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- Guided Learning Convolution System for DCASE 2019 Task 459
The system submitted to DCASE2019 task 4: sound event detection (SED) in domestic environments with a convolutional neural network with an embedding-level attention pooling module achieves the best performance compared to those of other participates.
- Robust human tracking based on multi-cue integration and mean-shift58
An adaptive multi-cue integration based Mean-Shift framework, motivated by the idea of tuning weight of each cue in an adaptive way to overcome the rigidity of the direct integration method, is proposed.
- Feature Completion Transformer for Occluded Person Re-Identification57
This work presents Feature Completion Transformer (FCFormer) that reduces noise interference and complements missing features in occluded parts and outperforms the state-of-the-art methods by significant margins on Occluded-Duke dataset.
- Defect detection of PCB based on Bayes feature fusion56
This framework has achieved good results in speed and accuracy and has been verified that the accuracy of the verification set is improved compared to the individual features using the fused features.
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- Multitask Learning of Time-Frequency CNN for Sound Source Localization54
A novel binaural SSL method based on time–frequency convolutional neural network (TF-CNN) with multitask learning is proposed to simultaneously localize azimuth and elevation under unknown acoustic conditions and achieves preferable localization performance compared with other popular methods.
- Depth Context: a new descriptor for human activity recognition by using sole depth sequences54
A local point detector is developed by sampling local points based on both motion and shape clues to represent human activities in depth sequences and is robust to partial occlusions in depth data, and also robust to the changes of pose, illumination and background to some extent.
- Weakly-Supervised 3D Human Pose Estimation With Cross-View U-Shaped Graph Convolutional Network53
A simple yet effective pipeline for weakly-supervised cross-view 3D human pose estimation by only using two camera views, requiring no 3D ground truth but only 2D annotations, which outperforms existing state-of-the-art methods remarkably.
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- Multi-Temporal Depth Motion Maps-Based Local Binary Patterns for 3-D Human Action Recognition51
A local spatio-temporal descriptor for action recognistion from depth video sequences, which is capable of distinguishing similar actions as well as coping with different speeds of actions is presented.
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- Robust 3D Action Recognition Through Sampling Local Appearances and Global Distributions46
A novel two-layer Bag-of-Visual-Words (BoVW) model, which suppresses the noise disturbances and jointly encodes both motion and shape cues, which is effective in distinguishing similar actions and robust to background clutter, partial occlusions and pepper noise.
- Semantic-aware Consistency Network for Cloth-changing Person Re-Identification45
A Semantic-aware Consistency Network (SCNet) to learn identity-related semantic features by proposing effective consistency constraints is presented, which generates the black-clothing image by erasing pixels in the clothing area, which explicitly mitigates the interference from clothing variations.
- Convolutional neural network‐based multi‐label classification of PCB defects45
A multi-task CNN model is proposed to handle the multi-label learning problem by defining each label learning as a binary classification task by customising the loss function.
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- Online RGB-D person re-identification based on metric model update43
An online re-id framework that makes full use of both appearance and skeleton information provided by RGB-D sensors, a novel feature funnel model (FFM) is proposed and comparison studies show the approach is more effective and adaptable to varying environments.
- Co-Evolution of Pose and Mesh for 3D Human Body Estimation from Video42
A Pose and Mesh Co-Evolution network (PMCE) is proposed that outperforms previous state-of-the-art methods in terms of both per-frame accuracy and temporal consistency on three benchmark datasets: 3DPW, Human3.6M, and MPI-INF-3DHP.
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- An Adaptive Method Based on Multiscale Dilated Convolutional Network for Binaural Speech Source Localization41
Experimental results show the proposed method can adaptively locate speech sources in simulated noisy and reverberant environments.
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- A compact representation of human actions by sliding coordinate coding37
This article proposes to encode the relative position of visual words using a simple but very compact method called sliding coordinates coding (SCC), which is more compact than many of the spatial or spatial–temporal pooling methods in the literature.
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- Merge-and-Split Graph Convolutional Network for Skeleton-Based Interaction Recognition34
The Merge-and-Split Graph Convolutional Network takes a unique perspective, treating interaction recognition as a global problem, and leverages a Merge-and-Split Graph structure to effectively capture dependencies between interaction body parts.
- Focusing Fine-Grained Action by Self-Attention-Enhanced Graph Neural Networks With Contrastive Learning34
This work first attempts to bring angular information as a complement to familiar joint and bone information, while learning the potential dependencies of the three kinds of information using graph neural networks, and proposes a self-attention-enhanced graph neural network (SAE-GNN), which outperforms state-of-the-art methods in recognizing fine-grained actions.
- Modified minimum squared error algorithm for robust classification and face recognition experiments34
For the first time, the modified minimum squared error classification (MMSEC) algorithm simultaneously predicts the class labels of the test sample and the training samples nearest to it and combines the predicted results to ultimately classify thetest sample.
- Unsupervised Monocular Visual-inertial Odometry Network33
An unsupervised visual-inertial odometry framework (UnVIO) is proposed, which is able to predict the per-frame depth map, as well as extracting and self-adaptively fusing visual-inertial motion features from image-IMU stream to achieve long-term odometry task.
- Multi-modal Multi-layer Fusion Network with Average Binary Center Loss for Face Anti-spoofing32
A novel Multi-modal Multi-layer Fusion Convolutional Neural Network (mmfCNN), which targets at finding a discriminative model for recognizing the subtle differences between live and spoof faces, and utilizes a multi-layer fusion model to further aggregate the features from different layers.
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- HTNet: Human Topology aware network for 3d Human pose estimation31
A novel Human Topology aware Network (HTNet) is proposed, which adopts a channel-split progressive strategy to sequentially learn the structural priors of the human topology from multiple semantic levels: joint, part, and body.
- Bringing Botany into Focus: Addressing Plant Blindness in Undergraduates Through an Immersive Botanical Experience31
Evidence is provided that participation in a botanical experience as part of a general biology course can positively shift undergraduates’ perception of botany, the study of plants.
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- Ordinal Constrained Binary Code Learning for Nearest Neighbor Search24
A novel ranking-preserving hashing method, dubbed Ordinal Constraint Hashing (OCH), which efficiently learns the optimal hashing functions with a graph-based approximation to embed the ordinal relations through a small data set (such as clusters or random samples).
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- Action Disambiguation Analysis Using Normalized Google-Like Distance Correlogram18
Normalized Google-Like Distance (NGLD) is proposed to numerically measuring this co-occurrence, due to its effectiveness in semantic correlation analysis and is proved a much richer descriptor by observably reducing action ambiguity in experiments, conducted on WEIZMANN dataset and the more challenging UCF sports.
- Interweaved Graph and Attention Network for 3D Human Pose Estimation17
A novel Interweaved Graph and Attention Network (IGANet) is proposed that allows bidirectional communications between graph convolutional networks (GCNs) and attentions and introduces an IGA module, where attentions are provided with local information from GCNs and GCNs are injected with global information from attentions.
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- A novel hierarchical Bag-of-Words model for compact action representation16
This work proposes to compress residual vectors into low-dimensional residual histograms by the simple but efficient BoW quantization, which yields a hierarchical BoW (HBoW) model which is not only compact but also informative.
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- FSAR: Federated Skeleton-based Action Recognition with Adaptive Topology Structure and Knowledge Distillation13
This paper pioneers a novel Federated Skeleton-based Action Recognition (FSAR) paradigm, which enables the construction of a globally generalized model without accessing local sensitive data, and introduces an Adaptive Topology Structure (ATS), separating generalization and personalization by learning a domain-invariant topology shared across clients and a domain-specific topology decoupled from global model aggregation.
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- APP: Adaptive Pose Pooling for 3D Human Pose Estimation from Videos12
The results show that the proposed Adaptive Pose Pooling (APP) module consistently enhances the performance of lifting models, achieving state-of-the-art results.
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- Gator: Graph-Aware Transformer with Motion-Disentangled Regression for Human Mesh Recovery from a 2D Pose9
A novel solution that contains an encoder of Graph-Aware Transformer and a decoder with Motion-Disentangled Regression to explore multiple relations during the evolution from skeleton to mesh, including joint-joint, joint-vertex and vertices relations, which often leads to implausible results.
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- Human activity prediction by mapping grouplets to recurrent Self-Organizing Map9
Experimental results confirm that the method is very efficient for predicting human activity and yields better performance than state-of-the-art works.
- Inferring Ongoing Human Activities Based on Recurrent Self-Organizing Map Trajectory9
The Recurrent SelfOrganizing Map (RSOM), which was designed to process sequential data, is novelly adopted in this paper for the high-level representation of ongoing activities and the innovation lies that observed features and their spatio-temporal contexts are encoded in a trajectory of the pre-trained RSOM units.
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- A binaural sound source localization model based on time-delay compensation and interaural coherence8
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- ARTS: Semi-Analytical Regressor using Disentangled Skeletal Representations for Human Mesh Recovery from Videos7
A novel semi-Analytical Regressor using disenTangled Skeletal representations for human mesh recovery from videos, called ARTS, which surpasses existing state-of-the-art video-based methods in both per-frame accuracy and temporal consistency on popular benchmarks.
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- Self-Refining Deep Symmetry Enhanced Network for Rain Removal6
Deep Symmetry Enhanced Network (DSEN) is proposed that is able to explicitly extract the rotation equivariant features from rain images and a self-refining mechanism to remove the accumulated rain streaks in a coarse-to-fine manner.
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- NanoHTNet: Nano Human Topology Network for Efficient 3D Human Pose Estimation5
A Nano Human Topology Network (NanoHTNet), a tiny 3D HPE network with stacked Hierarchical Mixers to capture explicit features and Efficient Temporal-Spatial Tokenization (ETST) is introduced to enhance spatio-temporal interaction and reduce computational complexity significantly.
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- Uncertainty-Aware Testing-Time Optimization for 3D Human Pose Estimation4
This work proposes an Uncertainty-Aware testing-time Optimization (UAO) framework, which keeps the prior information of pre-trained model and alleviates the overfitting problem using the uncertainty of joints, and utilizes the uncertainty of each joint to determine how much each joint is allowed for optimization.
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- Exploring Discriminative Views for 3D Object Retrieval4
This paper focuses on the 2D projective views that represent the 3D objects and proposes a boosting approach by evaluating the discriminative ability of each object's views to boost the multi-model graph learning method.
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- Self-distillation with Online Diffusion on Batch Manifolds Improves Deep Metric Learning–
This paper proposes a simple but effective Progressive Self-Distillation (PSD), which distills the knowledge progressively from the model itself during training, and extends it with an Online Batch Diffusion Process (OBDP), which is to capture the local geometric structure of manifolds in each batch, so that it can reveal the intrinsic relationships among samples in the batch and produce better soft distance targets.
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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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