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Claim this profileWorks364 from public data
- Improving person re-identification by attribute and identity learning888
An attribute-person recognition (APR) network is proposed, a multi-task network which learns a re-ID embedding and at the same time predicts pedestrian attributes, and demonstrates that by learning a more discriminative representation, APR achieves competitive re-IDs performance compared with the state-of-the-art methods.
- Drug–target interaction prediction: databases, web servers and computational models653
In this review, databases and web servers involved in drug-target identification and drug discovery are summarized, and some state-of-the-art computational models for drug- target interactions prediction, including network-based method, machine learning- based method and so on are introduced.
- Unsupervised Person Re-identification636
A progressive unsupervised learning (PUL) method to transfer pretrained deep representations to unseen domains and demonstrates that PUL outputs discriminative features that improve the re-ID accuracy.
- Long non-coding RNAs and complex diseases: from experimental results to computational models567
Some state-of-the-art computational models are introduced, which could be effectively used to identify disease-related lncRNAs on a large scale and select the most promising disease- related lnc RNAs for experimental validation and discussed the future directions of developing computational models for lncRNA research.
- Deep Multi-View Enhancement Hashing for Image Retrieval503
This paper proposes a supervised multi-view hash model which can enhance the multi- view information through neural networks, and significantly outperforms the state-of-the-art single-view and multi-View hashing methods.
- CNNs-Based RGB-D Saliency Detection via Cross-View Transfer and Multiview Fusion423
A novel framework based on convolutional neural networks (CNNs), which transfers the structure of the RGB-based deep neural network to be applicable for depth view and fuses the deep representations of both views automatically to obtain the final saliency map is proposed.
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- WBSMDA: Within and Between Score for MiRNA-Disease Association prediction376
The model of Within and Between Score for MiRNA-Disease Association prediction (WBSMDA) was developed to predict potential miRNAs associated with various complex diseases and would be a useful resource for potential miRNA-disease association identification.
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- Each Part Matters: Local Patterns Facilitate Cross-View Geo-Localization329
It is argued that neighbor areas can be leveraged as auxiliary information, enriching discriminative clues for geo-localization, and introduced a simple and effective deep neural network, called Local Pattern Network (LPN), to take advantage of contextual information in an end-to-end manner.
- Frequency-Aware Feature Fusion for Dense Image Prediction325
Comprehensive visualization and quantitative analysis demonstrate that FreqFusion effectively improves feature consistency and sharpens object boundaries, and extensive experiments across various dense prediction tasks confirm its effectiveness.
- Memory Matching Networks for One-Shot Image Recognition310
Memory Matching Networks (MM-Net) is presented - a novel deep architecture that explores the training procedure, following the philosophy that training and test conditions must match, and improves one-shot accuracy on Omniglot and miniImageNet.
- Unsupervised Person Re-Identification via Softened Similarity Learning307
The iterative training mechanism is followed but clustering is discarded, since it incurs loss from hard quantization, yet its only product, image-level similarity, can be easily replaced by pairwise computation and a softened classification task.
- Gait Recognition in the Wild with Dense 3D Representations and A Benchmark289
This paper proposes a novel framework to explore the 3D Skinned Multi-Person Linear (SMPL) model of the human body for gait recognition, named SMPLGait, and provides 3D SMPL models recovered from video frames which can provide dense 3D information of body shape, viewpoint, and dynamics.
- Depth Image Denoising Using Nuclear Norm and Learning Graph Model289
A group-based nuclear norm and learning graph (GNNLG) model, where for each patch, the most similar patches within a searching window are found, which is superior to other current state-of-the-art denoising methods in both subjective and objective criterion.
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- Constructing lncRNA functional similarity network based on lncRNA-disease associations and disease semantic similarity241
Two novel lncRNA functional similarity calculation models (LNCSIM) are developed based on the assumption that functionally similar lncRNAs tend to be associated with similar diseases and some of the predictive results for colorectal cancer and lung cancer were verified by independent biological experimental studies.
- Age-Invariant Face Recognition by Multi-Feature Fusionand Decomposition with Self-attention238
A novel Multi-feature Fusion and Decomposition framework for age-invariant face recognition, which learns more discriminative and robust features and reduces the intra-class variants and improves the AIFR performance than previous state-of-the-art methods.
- HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction232
The computational model of Heterogeneous Graph Inference for MiRNA-Disease Association prediction (HGIMDA) is developed to uncover potential miRNA-disease associations by integrating miRNA functional similarity, disease semantic similarity, Gaussian interaction profile kernel similarity, and experimentally verified miRNAs associations into a heterogeneous graph.
- Asymptotic Soft Filter Pruning for Deep Convolutional Neural Networks191
An asymptotic soft filter pruning (ASFP) method to accelerate the inference procedure of the deep neural networks and shows the effectiveness of the ASFP on image classification benchmarks.
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- Precise No-Reference Image Quality Evaluation Based on Distortion Identification183
This article proposes a novel scheme for precise NR IQA, which includes two successive steps, i.e., distortion identification and targeted quality evaluation, and employs the well-known Inception-ResNet-v2 neural network to train a classifier that classifies the possible distortion in the image into the four most common distortion types.
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- RBMMMDA: predicting multiple types of disease-microRNA associations175
To this knowledge, RBMMMDA is the first model which could computationally infer association types of miRNA-disease pairs and based on this model, not only new miRNAs, but also corresponding association types are obtained.
- Rapid and robust two-dimensional phase unwrapping via deep learning173
A deep convolutional neural network (DCNN) based method to perform rapid and robust two-dimensional phase unwrapping, with noise suppression and strong feature representation capabilities, which out-performed the conventional phase unwrap algorithms.
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- Parallel deblocking filter for HEVC on many‐core processor123
Experiments show that the proposed three-step parallel framework for HEVC dramatically accelerates more than the state-of-the-art parallel method.
- Beyond the Parts: Learning Multi-view Cross-part Correlation for Vehicle Re-identification109
The PCRNet explores vehicle parsing to learn discriminative part-level features, model the correlation among vehicle parts, and achieve precise part alignment for vehicle Re-Id, and proposes a self-supervised part prediction loss to make the GCNs generate features of invisible parts from visible parts under different viewpoints.
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- Efficient parallel HEVC intra‐prediction on many‐core processor90
Experiments show that the proposed method dramatically accelerates more than the state-of-the-art parallel method for HEVC IP.
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- ABINet++: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Spotting88
This paper argues that the limited capacity of language models comes from 1) implicit language modeling; 2) unidirectional feature representation; and 3) language model with noise input, and proposes an autonomous, bidirectional and iterative ABINet++ for scene text spotting.
- Recurrent Attention Model for Pedestrian Attribute Recognition88
This paper proposes end-to-end Recurrent Convolutional (RC) and Recurrent Attention (RA) models, which are complementary to each other and achieves state-of-the-art results, based on pedestrian attribute datasets, i.e. standard PETA and RAP datasets.
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- Watching a Small Portion could be as Good as Watching All: Towards Efficient Video Classification86
An end-to-end deep reinforcement approach which enables an agent to classify videos by watching a very small portion of frames by incorporating an adaptive stop network to measure confidence score and generate timely trigger to stop the agent watching videos, which improves efficiency without loss of accuracy.
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- SDPL: Shifting-Dense Partition Learning for UAV-View Geo-Localization78
A dense partition strategy (DPS) is proposed, dividing the image into multiple parts to explore contextual information while explicitly maintaining the global structure, and the shifting-fusion strategy, which generates multiple sets of parts in parallel based on various segmentation centers.
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- Learning Cross-View Geo-Localization Embeddings via Dynamic Weighted Decorrelation Regularization71
This work argues that minimizing embedding redundancy is a pivotal factor in enhancing a model’s ability to discriminate diverse scene characteristics and introduces a straightforward yet effective regularization technique, termed dynamic weighted decorrelation regularization (DWDR), which significantly outperforms conventional baselines in stringent conditions.
- From Speaker to Dubber: Movie Dubbing with Prosody and Duration Consistency Learning71
This work proposes a two-stage dubbing method that allows the model to first learn pronunciation knowledge before practicing it in movie dubbing, and outperforms several state-of-the-art methods on two primary benchmarks.
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- A Patent Analysis of Prognostics and Health Management (PHM) Innovations for Electrical Systems65
This series of survey papers reviews the history of PHM research in industry and its current status based on U.S. PHM patents from 2000 to 2015; discusses the application ofPHM in the design, manufacture, and deployment of equipment in various industries; and presents some the key research questions which remain to be addressed.
- Parsing is All You Need for Accurate Gait Recognition in the Wild64
The experimental results show a significant improvement in accuracy brought by the GPS representation and the superiority of ParsingGait, the first parsing-based dataset for gait recognition in the wild, by extending the large-scale and challenging Gait3D dataset.
- Ecological Cooperative Adaptive Cruise Control for a Heterogeneous Platoon of Heavy-Duty Vehicles With Time Delays64
The simulation results demonstrate that the proposed Eco-CACC strategy can significantly improve the fuel economy of heterogeneous platoon and the fuel saving performance of the proposed control strategy.
- Benchmarking Deep Learning Models and Automated Model Design for COVID-19 Detection with Chest CT Scans63
An automated deep learning methodology is designed to generate a lightweight deep learning model MNas3DNet41 that achieves an accuracy of 87.14%, F1-score of 86.25%, and AUC of 0.957, which are on par with the best models made by AI experts.
- Gait Recognition in the Wild with Multi-hop Temporal Switch61
A novel multi-hop temporal switch method to achieve effective temporal modeling of gait patterns in real-world scenes and achieves high efficiency with fewer model parameters and reduces the difficulty in optimization compared with 3D convolution-based models.
- Video Description with Spatial-Temporal Attention60
The proposed spatial-temporal attention (STAT) method takes advantage of object-level local features to address the problem of detail missing and outperforms the state-of-the-art methods on MSVD with BLEU4 score 0.511, and achieves superior BLEu4 score0.374 on MSR-VTT-10K.
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- Quality-Aware Selective Fusion Network for V-D-T Salient Object Detection58
A quality-aware selective fusion network (QSF-Net) to conduct VDT salient object detection, which contains three subnets including the initial feature extraction subnet, the quality-aware region selection subnet, and the region-guided selective fusion subnet.
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- FPGA-based accelerator for object detection: a comprehensive survey58
The current hardware accelerators are introduced and compared, the typical deep learning-based object detectors are summarized, and the challenges of object detection algorithms, hardware, and co-design are presented.
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- Self-supervised Cross-view Representation Reconstruction for Change Captioning51
A self-supervised cross-view representation reconstruction (SCORER) network that reconstruct the representations of unchanged objects by cross-attention, thus learning a stable difference representation for caption generation and devise a cross-modal backward reasoning to improve the quality of caption.
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- Semantic Relation-aware Difference Representation Learning for Change Captioning50
A semantic relation-aware difference representation learning network to explicitly learn the difference representation in the existence of distractors is proposed and an attention-based visual switch is devised to dynamically use visual information for caption generation.
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- Rethinking Boundary Discontinuity Problem for Oriented Object Detection46
A dual-optimization paradigm for angles is proposed, which for the first time achieves the objectives of both correcting angular boundary and blending angle with other parameters, and extensive experiments show that boundary discontinuity problem is well-addressed.
- Automated Model Design and Benchmarking of Deep Learning Models for COVID-19 Detection with Chest CT Scans43
A differentiable neural architecture search (DNAS) framework is proposed to automatically search the 3D DL models for 3D chest CT scans classification and use the Gumbel Softmax technique to improve the search efficiency and verify that CAM can be well applied in CovidNet3D for COVID-19 datasets to provide interpretability for medical diagnosis.
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- Improving Dynamic HDR Imaging with Fusion Transformer42
This paper proposes a transformer model for HDR imaging that outperforms the state-of-the-art by large margins on several popular public datasets, and demonstrates that the key component is the HDR transformer module.
- Hybrid Spectral Denoising Transformer with Guided Attention42
A hybrid approach that combines the advantages of both models with a Spatial-Spectral Separable Convolution (S3Conv), Guided Spectral Self-Attention (GSSA), and Self-Modulated Feed-Forward Network (SM-FFN) for hyperspectral image denoising.
- Stroke prediction from electrocardiograms by deep neural network42
The results indicate that ECG is a valuable complementary technique for stroke diagnostics and a data-driven classifier-Dense convolutional neural Network (DenseNet) for stroke prediction based on 12-leads ECG data is proposed.
- Cascaded Revision Network for Novel Object Captioning42
This paper proposes a novel network structure, Cascaded Revision Network, which aims at relieving the problem of inaccurate captioning by equipping the model with out-of-domain knowledge and can accurately describe images with unseen objects.
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- Image classification base on PCA of multi-view deep representation40
This paper proposes an image classification technology using principal component analysis based on multi-view depth characters using the SVM and shows that the method has good performance.
- Predict MiRNA-Disease Association with Collaborative Filtering40
The novel improved collaborative filtering-based miRNA-disease association prediction (ICFMDA) approach is proposed and it is hoped that ICFMDA would be useful in future miRNA and brain researches, and achieve better understanding of the nervous system in molecular level, cellular level, cell change process, and thus can support the research of human brain.
- Deep learning based basketball video analysis for intelligent arena application40
A deep learning based video analysis scheme for intelligent basketball arena applications that can automatically select the camera to give high-quality broadcast in real-time and detect the scoring clips as the highlight video reels to support the wonderful actions replay and online sharing functions is proposed.
- A 3.55‐µm Ultrathin, Skin‐Like Mechanoresponsive, Compliant, and Seamless Ionic Conductive Electrode for Epidermal Electrophysiological Signal Acquisition and Human‐Machine Interaction38
An ultra‐thin and dry electrode consisting of deep eutectic solvent (DES) and zwitterion (CEAB) electrodes, which exhibit significantly lower reactance and noise in both static and dynamic monitoring compared to standard Ag/AgCl gel electrodes are reported.
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- Practical guidelines for cell segmentation models under optical aberrations in microscopy37
This study evaluates cell image segmentation models under optical aberrations from fluorescence and bright field microscopy, and innovatively proposes the Point Spread Function Image Label Classification Model (PLCM), which can quickly and accurately identify aberration types and amplitudes from PSF images, assisting researchers without optical training.
- Optimizing sparse tensor times matrix on GPUs36
This work optimizes tensor-times-dense matrix multiply (Ttm) for general sparse and semi-sparse tensors on CPU and NVIDIA GPU platforms, and designs an in-place sequential SpTtm to avoid explicit data reorganizing between a tensor and a matrix in its conventional approach.
- Deep learning in biomedical image analysis35
This chapter briefly overviews various deep learning models and their learning principles, and demonstrates the hands-on experience of developing deep neural networks for medical image analysis with several typical applications such as feature representation learning, image segmentation, and image registration.
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- Exploring complex and heterogeneous correlations on hypergraph for the prediction of drug-target interactions32
A heterogeneous hypergraph-based framework for drug-target interaction predictions by modeling biological networks through a hypergraph, where each vertex represents a drug or a target and a hyperedge indicates existing similar interactions or associations between the connected vertices, suggests that HHDTI is a scalable and practical tool for uncovering novel drug- target interactions.
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- Real-World Automatic Makeup via Identity Preservation Makeup Net21
This paper introduces a new makeup model, called Identity Preservation Makeup Net (IPM-Net), which preserves not only the background but the critical patterns of the original identity, and disentangle the face images to two different information codes, i.e., identity content code and makeup style code.
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- Mask and Predict15
A novel multi-step reasoning manner for SGG is proposed, which breaks SGG into two explicit learning stages, including intuitive training stage (ITS) and rationalTraining stage (RTS), yielding an intuitive scene graph.
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- Dual-View Ranking with Hardness Assessment for Zero-Shot Learning12
A novel DuAl-view RanKing (DARK) loss for zeroshot learning simultaneously ranking labels for an image by point-to-point metric and ranking images for a label by pointto-set metric is proposed, which is capable of better modeling the relationship between images and classes.
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- Multi-stage reasoning on introspecting and revising bias for visual question answering5
A bias discrimination module is proposed to distinguish good and bad bias indiscriminately in existing debiasing methods and helps with feature updating for both image regions and question words, thereby facilitating more accurate and comprehensive reasoning about both the image content and question.
- Predicting Protein–Protein Interactions Based on Ensemble Learning-Based Model from Protein Sequence5
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- Evolution of ICTs-empowered-identification: A general re-ranking method for person re-identification4
A Feature Relation Map to mine the latent relation between the k -neighbors through convolution neural network is introduced, and a metric learning based Similarity Evaluation (SE) model is proposed to obtain the re-ranking distance from the FRM by incorporating the dilated convolution and normal convolution kernels.
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- Counterfactual GAN for debiased text-to-image synthesis3
A new debiased text-to-image synthesis method with a counterfactual learning scheme that conceptualizes the process as calculating the Natural Direct Effect on the generated visual features and use the Total Indirect Effect to produce the final image for debiased text-to-image generation.
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- Technique Report of CVPR 2024 PBDL Challenges2
This technical report summarizes the outcomes of the Physics-Based Vision Meets Deep Learning (PBDL) 2024 challenge, held in CVPR 2024 workshop, focusing on Low-Light Enhancement and Detection as well as High Dynamic Range (HDR) Imaging.
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- Language-Assisted Siamese Contrastive Framework for Fine-Grained Remote Sensing Ship Image Retrieval1
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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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