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- 403
- The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification401
Experimental results show the superiority of the MC-Loss when implemented on top of common base networks can achieve state-of-the-art performance on all four fine-grained categorization datasets, and ablative studies further demonstrate the supremacy of the loss when compared with other recently proposed general-purpose losses for visual classification.
- Fine-Grained Visual Classification via Progressive Multi-granularity Training of Jigsaw Patches389
This work proposes a novel framework for fine-grained visual classification with a progressive training strategy that effectively fuses features from different granularities, and a random jigsaw patch generator that encourages the network to learn features at specificgranularities.
- Comments on "Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Application to Face and Palm Biometrics"387
This communication shows that the basic ideas of UDP and LPP are identical, and UDP is just a simplified version of LPP on the assumption that the local density is uniform.
- 338
- A Survey on Long-Tailed Visual Recognition215
This survey focuses on the problems caused by long-tailed data distribution, sort out the representative long-tails visual recognition datasets and summarize some mainstream long-tail studies, and quantitatively study 20 widely-used and large-scale visual datasets proposed in the last decade.
- Cross-Domain Recommendation via Cluster-Level Latent Factor Model204
A novel cluster-level based latent factor model is proposed to enhance the cross-domain recommendation, which can not only learn the common rating pattern shared across domains with the flexibility in controlling the optimal level of sharing, but also learn the domain-specific rating patterns of users in each domain that involve the discriminative information propitious to performance improvement.
- Building Dual-Domain Representations for Compression Artifacts Reduction201
This work proposes a highly accurate approach to remove artifacts of JPEG-compressed images that jointly learns a very deep convolutional network in both DCT and pixel domains and shows large improvements over the state of the arts.
- Patients with COVID-19 in 19 ICUs in Wuhan, China: a cross-sectional study194
Critically ill patients with COVID-19 are associated with a higher risk of severe complications and need to receive an intensive level of treatments, and these patients are investigated in a multicenter prospective observational study.
- Monthly streamflow forecasting based on improved support vector machine model184
An improved SVM model with adaptive insensitive factor with phase-space reconstruction theory is proposed to improve the performance of the support vector machine (SVM) model in predicting monthly streamflow, and is of better generalization ability and higher prediction accuracy.
- 181
- Text extraction from natural scene image: A survey175
This paper offers the researchers a link to public image database for the algorithm assessment of text extraction from natural scene images and draws attention to studies on the first two steps in the extraction process, since OCR is a well-studied area where powerful algorithms already exist.
- Self-Supervised Convolutional Subspace Clustering Network172
An end-to-end trainable framework that combines a dual self-supervision that exploits the output of spectral clustering to supervise the training of the feature learning module and the self-expression module into a joint optimization framework is proposed.
- Your “Flamingo” is My “Bird”: Fine-Grained, or Not155
The key intuition that: coarse- level label prediction exacerbates fine-grained feature learning, yet fine-level feature betters the learning of coarse-level classifier is discovered, which enables the design of a very simple albeit surprisingly effective solution to a new problem.
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- The Role of Data Analysis in the Development of Intelligent Energy Networks145
More comprehensive data analysis methods are needed to handle the increasing amount of data and to mine more valuable information in intelligent energy networks.
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- SketchMate: Deep Hashing for Million-Scale Human Sketch Retrieval130
This work proposes a deep hashing framework for sketch retrieval that, for the first time, works on a multi-million scale human sketch dataset and shows that state-of-the-art hashing models specifically engineered for static images fail to perform well on temporal sketch data.
- Decorrelation of Neutral Vector Variables: Theory and Applications121
Two fundamental invertible transformations, namely, serial non linear transformation and parallel nonlinear transformation, are proposed to carry out the decorrelation, which shows that a highly negatively correlated neutral vector can be transformed to a set of mutually independent scalar variables with the same degrees of freedom.
- Cluster-Guided Asymmetric Contrastive Learning for Unsupervised Person Re-Identification120
This paper proposes a Cluster-guided Asymmetric Contrastive Learning (CACL) approach for unsupervised person Re-ID, in which clustering result is leveraged to guide the feature learning in a properly designed asymmetric contrastive learning framework.
- 117
- Efficient Face Super-Resolution via Wavelet-based Feature Enhancement Network115
A wavelet-based feature enhancement network is proposed, which mitigates feature distortion by losslessly decomposing the input feature into high and low-frequency components using the wavelet transform and processing them separately.
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- Robust, accurate and efficient face recognition from a single training image: A uniform pursuit approach107
Experimental results show that the robustness, accuracy and efficiency of the proposed UP method compare favorably to the state-of-the-art one sample based methods.
- 102
- Adapting User Preference to Online Feedback in Multi-round Conversational Recommendation98
Experimental results on two benchmarks showed that FPAN outperformed the state-of-the-art user preference models in CRS, and the multi-round CRS can also be enhanced by using FPAN as its recommender component.
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- One-To-Many Network for Visually Pleasing Compression Artifacts Reduction92
A one-to-many network, which measures output quality using a perceptual loss, a naturalness loss, and a JPEG loss is proposed, which avoids grid-like artifacts during deconvolution using a shift-and-average strategy.
- Cross-modal subspace learning for fine-grained sketch-based image retrieval90
Through thorough examination of the experimental results, it is demonstrated that the subspace learning can effectively model the sketch-photo domain-gap, and a few key insights to drive future research are drawn.
- An empirical convolutional neural network approach for semantic relation classification88
This work deals with the relation classification task utilizing a convolutional neural network approach to automatically control feature learning from raw sentences and minimize the application of external toolkits and resources and shows that the proposed architecture significantly outperforms the state-of-the-art systems.
- Fine-grained face verification: FGLFW database, baselines, and human-DCMN partnership82
A Deep Convolutional Maxout Network (DCMN) is developed which aim to tolerate the multi-modal intra-personal variations and distinguish fine-grained localized inter-personal facial details and the experimental results suggest that the proposed DCMN method significantly outperforms current techniques such as Deepface, DeepID2, and VGG-Face.
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- HCL2000 - A Large-scale Handwritten Chinese Character Database for Handwritten Character Recognition74
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- Improving Abstractive Dialogue Summarization with Graph Structures and Topic Words67
A Topic-word Guided Dialogue Graph Attention (TGDGA) network is proposed to model the dialogue as an interaction graph according to the topic word information and a masked graph self-attention mechanism is used to integrate cross-sentence information flows and focus more on the related utterances, which makes it better to understand the dialogue.
- DNN Filter Bank Cepstral Coefficients for Spoofing Detection67
A new filter bank-based cepstral feature, deep neural network (DNN) filter bank cepStral coefficients, to distinguish between natural and spoofed speech, and the experimental results show that the Gaussian mixture model maximum-likelihood classifier trained by the new feature performs better than the state-of-the-art linear frequency triangle filter bankcepstrals-based classifier, especially on detecting unknown attacks.
- Aspect-based latent factor model by integrating ratings and reviews for recommender system66
A novel model, called Aspect-based Latent Factor Model (ALFM) to integrate ratings and review texts via latent factor model, in which by integrating rating matrix, user-review matrix and item-attribute matrix, the user latent factors and item latent factors with word latent factors can be derived.
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- Progressive Co-Attention Network for Fine-Grained Visual Classification64
This paper calculates the channel-wise similarity by encouraging interaction between the feature channels within same-category image pairs to capture the common discriminative features and erase the prominent areas enhanced by the channel interaction to force the network to focus on other discriminating regions.
- Equidistant prototypes embedding for single sample based face recognition with generic learning and incremental learning64
Although it is fairly simple and parameter-free, LRA, combined with commonly used local descriptors, such as Gabor representation and local binary patterns, outperforms the state-of-the-art methods for several standard experiments on the Extended Yale B, CMU PIE, AR, and FERET databases.
- Vibrant fault diagnosis for hydroelectric generator units with a new combination of rough sets and support vector machine63
A novel classifier based on a novel combination of rough sets and support vector machine is proposed and applied in the fault diagnosis for HGU, and shows that the proposed classifier can more properly describe the complex map between the faults and their symptoms.
- The Eyes of the Gods: A Survey of Unsupervised Domain Adaptation Methods Based on Remote Sensing Data61
A comprehensive review is provided with a fine-grained taxonomy of UDA methods applied for remote sensing data, which includes Generative training, Adversarial training, Self-training and Hybrid training methods, to better assist scholars in understandingRemote sensing data and further advance the development of methods.
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- Image-text dual neural network with decision strategy for small-sample image classification59
This work shows how to efficiently and effectively utilize semantic information of the annotations to improve the performance of small-sample classification by incorporating a decision strategy into the image-text dual neural network to further improve theperformance of the original model on few-shot datasets.
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- Product comparison using comparative relations57
Experiments on popular electronic products show that the proposed approach outperforms the state-of-the-art methods.
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- OSLNet: Deep Small-Sample Classification With an Orthogonal Softmax Layer52
The proposed Orthogonal Softmax Layer (OSL), which makes the weight vectors in the classification layer remain orthogonal during both the training and test processes, has better performance than the methods used for comparison on four small-sample benchmark datasets, as well as its applicability to large-sample datasets.
- A Survey on Machine Learning‐Based Mobile Big Data Analysis: Challenges and Applications52
This paper attempts to identify the requirement and the development of machine learning-based mobile big data (MBD) analysis through discussing the insights of challenges in the mobile bigData, and reviews the state-of-the-art applications of data analysis in the area of MBD.
- A Cross-Domain Recommendation Model for Cyber-Physical Systems50
A novel cross-domain recommendation model, which not only learns the common rating pattern across domains with the flexibility in controlling the optimal level of sharing, but also learn the domain-specific rating patterns in each domain involving discriminative information propitious to performance improvement is proposed.
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- The small sample size problem of ICA: A comparative study and analysis46
A locality pursuit approach is introduced which applies the locality preserving projections in the high-dimensional whitened space and performs better than ICA and other conventional approaches, such as Eigenfaces, Laplacianfaces, and Fisherfaces.
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- Adversarial Network Bottleneck Features for Noise Robust Speaker Verification45
Experimental results on the RSR2015 database show that the proposed AN bottleneck feature (AN-BN) dramatically outperforms the STSA-MMSE and DNN-SE based MFCCs for different noise types and signal-to-noise ratios and is able to improve the speaker verification performance under the clean condition.
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- An Activation Force-based Affinity Measure for Analyzing Complex Networks41
An affinity network is found that compactly connects the cancer-associated proteins to each other, which may reveal novel information for cancer study; this includes likely protein interactions and key proteins in cancer-related signal transduction pathways.
- Fine-Grained Instance-Level Sketch-Based Video Retrieval40
This work proposes a novel cross-modal retrieval problem of fine-grained instance-level sketch-based video retrieval (FG-SBVR), where a sketch sequence is used as a query to retrieve a specific target video instance.
- Analysis of Key Factors in Heat Demand Prediction with Neural Networks40
A model based on Elman neural networks was adopted, of which the results can help heat producers to optimize their production and thus mitigate costs and the introduction of wind speed and direct solar radiation has opposite impacts on the performance of ENN.
- Gabor-Eigen-Whiten-Cosine: A Robust Scheme for Face Recognition40
The novelty of this paper lies in the finding that the same face with complex variations, projected into the Gabor based whitened PCA feature space, is approximately angle invariance.
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- A lightweight authentication scheme for telecare medical information system38
This paper combines Physical Unclonable Function (PUF) and Elliptic Curve Cryptography (ECC) technology to propose an access control and authentication scheme suitable for TMIS, and it is proved that the scheme is safe in the face of major attacks.
- CRISPR-M: Predicting sgRNA off-target effect using a multi-view deep learning network36
Experimental analysis of CRISPR-M under multiple metrics reveals its capability to extract features and validates its superiority on sgRNA off-target effect predictions, and demonstrates significant performance advantages running on real-world datasets.
- A holistic model of mining product aspects and associated sentiments from online reviews36
A novel model to realize aspect-based sentiment summarization in an integrative way is proposed: composing the system with consistently designed feature extraction and clustering, collocation orientation disambiguation, and sentence sentiment strength calculation.
- Multi-scale Joint Encoding of Local Binary Patterns for Texture and Material Classification35
A novel and effective multi-scale joint encoding of local binary patterns (MSJLBP) for texture and material classification and significantly outperforms the classical MS-LBP and achieves the state-of-the-art performance.
- Construction of semantic bootstrapping models for relation extraction34
This paper introduces formalization for this class of bootstrapping models, which allows semantic constraint to guide learning iterations and use flexible bottom-up kernel to compare patterns and demonstrates performance superior to the state of the art.
- GPCA: A Probabilistic Framework for Gaussian Process Embedded Channel Attention32
The Gaussian process embedded channel attention (GPCA) module is proposed and can be efficiently implemented and integrated into the end-to-end training of the CNNs with a mathematically tractable solution.
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- Harmonic estimation in power systems using an optimised adaptive Kalman filter based on PSO‐GA29
The proposed hybrid algorithm has more accurate estimation, faster convergence and better robustness against noise in comparison with the conventional Kalman filter (KF), KF based on the maximum likelihood and PSO aided KF (PSO-KF).
- A Study on the CBOW Model's Overfitting and Stability28
This study adds the regularized structure risk summation to the objective function of the CBOW model and proposes inverse word frequency encoding for theCBOW model to substantially improve the quality of word vectors.
- Instance-Level Coupled Subspace Learning for Fine-Grained Sketch-Based Image Retrieval27
This paper forms a cross-domain framework specifically designed for the task of FG-SBIR that simultaneously conducts instance-level retrieval and attribute prediction and proposes a joint view selection and attribute subspace learning algorithm to learn domain projection matrices for photo and sketch, respectively.
- CURI-YOLOv7: A Lightweight YOLOv7tiny Target Detector for Citrus Trees from UAV Remote Sensing Imagery Based on Embedded Device26
The proposed CURI-YOLOv7 model can basically achieve the function of individual tree detection in UAV remote sensing imagery on embedded devices and forms a foundation for the subsequent UAV real-time identification of the citrus tree with its geographic coordinates positioning, which is conducive to the study of precise agricultural management of citrus orchards.
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- Lighting-aware face frontalization for unconstrained face recognition26
A lighting-aware face frontalization method that aims to generate both lighting-recovered and lighting-normalized frontalized images, based on only five fiducial landmarks, which outperforms the deep learning based methods on the MPIE database under variable pose and lighting.
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- Emulating biological strategies for uncontrolled face recognition26
It is shown that a simple system designed by emulating biological strategies of human visual system can largely surpass the state-of-the-art performance on uncontrolled face recognition.
- Sea fog detection based on unsupervised domain adaptation25
This work proposes an unsupervised domain adaptation method to bridge the abundant labeled land fog data and the unlabeled sea fog data to realize the sea fog detection and achieves an accuracy of sea fog recognition up to 99.17%, which is nearly 3% higher than those vanilla methods.
- U-TRI: Unlinkability Through Random Identifier for SDN Network25
The result of experiments indicates that U-TRI (Unlinkability Through Random Identifier) is capable of defending traffic analysis with very little burdens on network performance.
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- A high-performance training-free approach for hand gesture recognition with accelerometer25
A high-performance training-free approach for hand gesture recognition with accelerometer that shows better performance than the traditional training-required algorithms of Hidden Markov Model (HMM) and Dynamic Time Warping (DTW).
- Multi Resource Scheduling Problem Based on an Improved Discrete Particle Swarm Optimization25
The results of simulation optimization experiments suggest that the improved discrete PSO algorithm proposed in this paper is well suited for quay crane scheduling in practice in container terminals.
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- Supervised latent Dirichlet allocation with a mixture of sparse softmax23
This work embeds a mixture of softmax model into latent Dirichlet allocation model, and proposes a supervised topic model, supervised latent Diriclet allocation with a mixtureof softmax, and its improved version, supervised Latent Dirichlets allocation with an improved version of sparse softmax.
- From one to many: Pose-Aware Metric Learning for single-sample face recognition23
The Pose-Aware Metric Learning method achieves an 100% accuracy on the Multi-PIE database under the test setting across poses, which is significantly better than the traditional methods that use a large generic image ensemble to learn the cross-pose transformations.
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- A Correlation Context-Driven Method for Sea Fog Detection in Meteorological Satellite Imagery22
A correlation context-driven method for sea fog detection is proposed in this letter, which mainly consists of a two-stage superpixel-based fully convolutional network (SFCNet), named SFCNet, and a fully connected Conditional Random Field (CRF) is utilized to model the dependencies between pixels.
- Dirichlet mixture modeling to estimate an empirical lower bound for LSF quantization22
The line spectral frequencies (LSFs) are commonly used for the linear predictive/autoregressive model in speech and audio coding and the probability density function (PDF)-optimized vector quantization (VQ) has been studied intensively for quantization of LSF parameters.
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- Text Similarity Computing Based on Standard Deviation22
A new algorithm to compute the text similarity based on standard deviation is put forward and experiments show the validity and the feasibility of the standard deviation-based algorithm.
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- ReMarNet: Conjoint Relation and Margin Learning for Small-Sample Image Classification19
This paper proposes to enhance the discriminative power of features from a new perspective by introducing a novel neural network termed Relation-and-Margin learning Network (ReMarNet), which assembles two networks of different backbones so as to learn the features that can perform excellently in both of the aforementioned two classification mechanisms.
- Learning a locality discriminating projection for classification19
A new algorithm called locality discriminating projection (LDP) for subspace learning is introduced, which provides a new scheme for discriminant analysis by considering both the manifold structure and the prior class information.
- Locality preserving and global discriminant projection with prior information19
A new algorithm called locality preserving and global discriminant projection with prior information (LPGDP) for dimensionality reduction and classification, by considering both the manifold structure and thePrior information, where the prior information includes not only the class label but also the misclassification of marginal samples.
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- Multi-label learning with prior knowledge for facial expression analysis18
A multi-label learning algorithm for FEA is proposed to solve the problem of depicting facial expressions more effectively and model FEA as a multi- label problem, which depicts all facial expressions with multiple continuous values and labels of predefined affective states.
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- Dual-Prior Augmented Decoding Network for Long Tail Distribution in HOI Detection17
This paper proposes a dual-prior augmented decoding network by decomposing the HOI task into two sub-tasks: human-object pair detection and interaction recognition and leverages external knowledge to enhance the model's ability at a finer granularity.
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- Line spectral frequencies modeling by a mixture of von Mises–Fisher distributions17
Experimental results show that the proposed VVQ outperforms the recently introduced Dirichlet mixture model-based VQ and the conventional Gaussian mixturemodel- based VQ, in terms of modeling performance and D-R relation.
- Mining activation force defined dependency patterns for relation extraction17
Experimental results demonstrate that the activation force based trigger word mining method plays an essential role in improving the performance, and the proposed method has good performance in the implementation of English Slot Filling 2013.
- Knowledge base completion by learning pairwise-interaction differentiated embeddings17
A Pairwise-interaction Differentiated Embeddings model is proposed to embed entities and relations in the knowledge base to low dimensional vector representations and then predict the possible truth of additional facts to extend theknowledge base.
- A query by humming system based on locality sensitive hashing indexes17
An index algorithm of note- based locality sensitive hashing (NLSH), a two-level filtering algorithm of NLSH and pitch-based localitysensitive hashing (PLSH) to screen candidate fragments, an algorithm of boundary alignment linear scaling (BALS) to locate the accurate boundary of candidates and an algorithm named key transposition recursive alignment (KTRA) to tackle the problem ofKey transposition.
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- Cross-sentence N-ary relation classification using LSTMs on graph and sequence structures16
An N-gram Graph L STM (NGG LSTM) network, which updates the hidden states by aggregating graph neighbor node information and the inherent sequence structural information of sentence to make full use of the relevant graph structure information.
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- Brake disc positioning and defect detection method based on improved Canny operator15
Due to uneven illumination, surface texture, or environmental dust, the extracted scratches exhibit fragmentation, and traditional morphological operations cannot handle scratches well, so an improved Canny operator combined with probabilistic Hough transform is proposed for scratch connection.
- Event detection from text using path-aware graph convolutional network15
PGCN is designed as flat structure to avoid over-smoothing problem, while path-aware aggregation is proposed to capture all-order contextual information and integrate dependency type information into feature space at the same time to deal with sparsity problem of some path types.
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- Constructing Maximum Entropy Language Models for Movie Review Subjectivity Analysis14
Detailed experiments given in this paper show that with well selected and weighted language features, MaxEnt models with exponential priors are significantly more suitable for the text subjectivity analysis task.
- Comment on "100% Accuracy in Automatic Face Recognition"14
It is argued that the feasibility of image averaging on identification documents is not fully supported by the presented evidence.
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- Segmentation Head Networks with Harnessing Self-Attention and Transformer for Insulator Surface Defect Detection13
A robust fast detection algorithm combined segmentation head networks with harnessing self-attention and transformer (HST-Net), which is based on the You Only Look Once (YOLO) v5 to recognize and assess the extent and types of damage on the insulator surface.
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- DS-UI: Dual-Supervised Mixture of Gaussian Mixture Models for Uncertainty Inference in Image Recognition13
Experimental results show the DS-UI outperforms the state-of-the-art UI methods in misclassification detection and finds statistically significant improvements in open-set out- of-domain/-distribution detection.
- Understanding Objects in Video: Object-Oriented Video Captioning via Structured Trajectory and Adversarial Learning13
The proposed STraNet, a video-based structured trajectory network via adversarial learning (STraNet) to effectively analyze the activities along the time domain and proactively capture the vision-language connections under small datasets, exhibits the ability of precisely describing the concurrent objects and their activities in details.
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- Artistic features extraction from chinese calligraphy works via regional guided filter with reference image13
An extraction method based on regional guided flter (RGF) with reference images, which is generated by KNN matting and used as the input image for RGF can extract more accurate and complete form and spirit with lower error rate.
- PRIS at TREC 2011 Microblog Track.13
The authors' system to Micro-blog Track at TREC2011 is described, which includes data obtaining and preprocessing, index building and query expansion, which includes Word Activation Force algorithm (WAF) and Electric Resistance Network.
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- Research on night-time vehicle target detection based on improved KSC-YOLO V512
An enhanced YOLOv5 algorithm designed to improve the detection of vehicles during night driving and ensure detection accuracy in low-light environments is introduced, which effectively navigates the challenges posed by inadequate lighting and vehicle blurring in nocturnal environments.
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- A temporal model in Electronic Health Record search12
A novel representation framework is designed to model the clinical narrative text as document sequence, where the textual and temporal dimension are modeled simultaneously, and a dynamic time warping based measure is proposed to quantify the similarity between EHRs of different patients.
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- Social Media Communication Model Research Bases on Sina-weibo12
Facing new challenges of understanding information propagation in microblog, the framework of information producing and receiving was proposed and a general model named competing-window is presented based on human behavior.
- Credible Association Rule and Its Mining Algorithm Based on Maximum Clique12
A novel concept of association pattern called credible association rule (CAR), in which each item has the same support level, in which the confidence directly reflects the credible degree of the rule instead of the traditional support.
- Monitoring visual focus of attention via local discriminant projection12
This paper presents a system to monitor a subject's Visual Focus of Attention (VFOA) based on his/her head poses using a local discriminant projection (LDP) method and indicates that this approach outperforms other methods.
- Utilizing graph neural networks to improving dialogue-based relation extraction11
A Relational Attention Enhanced Graph Convolutional Network (RAEGCN) is proposed, which constructs the whole dialogue as a semantic interactive graph by emphasizing the speaker-related information and leveraging various inter-sentence dependencies.
- Recovering dropped pronouns in Chinese conversations via modeling their referents11
This work presents a novel end-to-end neural network model based on a structured attention mechanism that models the referents of dropped pronouns utilizing both sentence-level and word-level information.
- Comparisons of Non-Gaussian Statistical Models in DNA Methylation Analysis11
Experimental results show that the non-Gaussian statistical model-based methods are superior to the conventional Gaussian distribution-based method and are meaningful tools for DNA methylation analysis.
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- Product ranking using hierarchical aspect structures10
This paper uses a hierarchical structure-based model to propagate and reassign aspect-based comparative opinions by using the parent-child and sibling relations between aspects in the product aspect hierarchy and employs graph-based ordering algorithms to consolidate these reassigned pairwise opinions into listwise comparison results.
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- Deep InterBoost networks for small-sample image classification9
Experimental results on four small-sample datasets, UIUC-Sports, LabelMe, 15Scenes and Caltech101, demonstrate that the proposed ensemble method outperforms existing ones and results from the Wilcoxon signed-rank tests show that the method is statistically significantly better than the methods compared.
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- Handwritten Chinese character recognition using Local Discriminant Projection with Prior Information9
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- Dual-attention Guided Dropblock Module for Weakly Supervised Object Localization8
The dual-attention guided dropblock module (DGDM) is proposed, which aims at learning the informative and complementary visual patterns for WSOL, which achieves new state-of-the-art localization performance.
- On the Comparisons of Decorrelation Approaches for Non-Gaussian Neutral Vector Variables8
This article extensively compare PNT with PCA and ICA through both theoretical analysis and experimental evaluations, and demonstrates the superiority of PNT for decorrelating the neutral vector variables.
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- A Multi-level System for Sequential Update Summarization8
A new multi-level system which can broadcasts with useful, new, and timely sentence-length updates about a developing event and incorporates techniques from topic-level and sentence-level summarization is presented.
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- Robust Discriminant Analysis of Latent Semantic Feature for Text Categorization7
A Discriminative Semantic Feature (DSF) method for vector space model based text categorization that can not only uncover latent semantic structure but also capture the discriminative feature.
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- Knowledge Transfer Based Fine-Grained Visual Classification6
A orthogonal loss (OR-loss) is proposed to encourage the network to find diverse and meaningful regions and is demonstrated the superiority of the proposed method and obtain state-of-the-art (SOTA) performances on three popular FGVC datasets.
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- Locality discriminating indexing for document classification6
Comparative experiments show that the proposed locality discriminating indexing algorithm for document classification isable to derives compact discriminating document representations for classification.
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- Hierarchical Visual Attribute Learning in the Wild5
This work proposes a model called the object-specific attribute relation net, which takes advantage of three types of relations among attributes - positive, negative, and hierarchical - to better facilitate attribute recognition in images and introduces several evaluation metrics for attribute hierarchy.
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- BALSON: BAYESIAN LEAST SQUARES OPTIMIZATION WITH NONNEGATIVE L1-NORM CONSTRAINT5
With the estimated posterior distributions, the original parameters can be effectively reconstructed in polynomial fitting problems, and the BALSON framework is found to perform better than conventional methods.
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- SiCL: Silhouette-Driven Contrastive Learning for Unsupervised Person Re-Identification with Clothes Change4
A semantic mask-driven contrastive learning approach is proposed, in which silhouette masks are embedded into contrastive learning framework as the semantic prompts and cross-clothes invariance is learnt from hierarchically semantic neighbor structure by combining both RGB and semantic features in a two-branches network.
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- Scalable NAS with factorizable architectural parameters4
This paper presents a scalable algorithm by factorizing a large set of candidate operators into smaller subspaces that allows to search for effective activation functions along with the regular operators including convolution, pooling, skip-connect, etc.
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- SEA: A Combined Model for Heat Demand Prediction4
STL-ENN-ARIMA (SEA), a combined model, was proposed based on the combination of the Elman neural network and the autoregressive integrated moving average model, which are commonly applied to heat demand prediction.
- Quantitative Comparisons of Linked Color Imaging and White-Light Colonoscopy for Colorectal Polyp Analysis4
A new criterion, namely entropy of colorGradients image (ECGI), which is based on color gradients distribution and provides a comprehensive and objective evaluating indicator of the performance of colorectal images is presented.
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- Image-Text Dual Model for Small-Sample Image Classification4
The proposed image-text dual model aims to utilize the text information to overcome the problem of training deep models on small-sample datasets and can achieve the highest image classification accuracy among all the referred models on LabelMe and UIUC-Sports.
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- Benchmarking Semantic Segmentation Models via Appearance and Geometry Attribute Editing3
An automatic data generation pipeline Gen4Seg is constructed to stress-test semantic segmentation models by generating various challenging samples with different attribute changes, and suggests the potential of generative models as effective tools for automatically analyzing segmentation models.
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- Bases sorting: Generalizing the concept of frequency for over-complete dictionaries3
An algorithm, called Bases Sorting, is proposed to sort the bases of over-complete dictionaries used in sparse representation according to the magnitudes of coefficients when representing the training samples, thus generalizing the traditional concept of frequency for over- complete dictionaries.
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- Unsupervised Sentiment-Bearing Feature Selection for Document-Level Sentiment Classification3
An unsupervised sentiment-bearing feature selection method (USFS) is proposed, which incorporates sentiment discriminant analysis (SDA) into sentiment strength calculation (SSC).
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- Robust Face Recognition from One Training Sample per Person3
The proposed Gabor-based PCA method using Whiten Cosine Similarity Measure for Face Recognition from One training Sample per Person achieves much higher accuracy than the standard Eigenface technique in the authors' large-scale experiment.
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- Online Regression of Grandmother-Cell Responses with Visual Experience Learning for Face Recognition2
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- Semi-supervised Learning Based on Label Propagation through Submanifold2
A semi-supervised learning algorithm is proposed based on label propagation through submanifold, whose optimal solution can minimize the cost function and satisfy the supervised constraint.
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- Gabor Feature Based Classification Using LDA/QZ Algorithm for Face Recognition2
Experimental results show the LDA/QZ algorithm is always the best or comparable to the best in term of recognition accuracy and the state-of-art dimension reduction algorithms and their combinations of Gabor feature for face recognition are performed.
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- Activation Force-based Air Pollution Observation Station Clustering1
This paper model the connections of the pollution sources by AF so that the relationship among the observation stations and the coincidence of the transmission links can be modeled effectively and conducts clustering of the air pollution sources via modularity measurement.
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- Transformed Principal Gradient Orientation for Robust and Precise Batch Face Alignment1
Experimental results show TPGO based batch alignment is more precise and robust than the state-of-the-art methods such as RASL and SIFT feature base Congealing, and integrated with a SIFT based pre-alignment procedure.
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- A Clustering Algorithm Based on Adaptive Subcluster Merging1
The results demonstrate that the proposed ASM based clustering algorithm performs better than K-means, complete-link hierarchial, density-based and maximum variance algorithms.
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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 2026-10-16. 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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