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Claim this profileWorks293 from public data
- MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition2,241
A benchmark task to recognize one million celebrities from their face images, by using all the possibly collected face images of this individual on the web as training data, which could lead to one of the largest classification problems in computer vision.
- Suppress and Balance: A Simple Gated Network for Salient Object Detection529
A novel gated dual branch structure is designed to build the cooperation among different levels of features and improve the discriminability of the whole network and adopts the atrous spatial pyramid pooling based on the proposed "Fold" operation (Fold-ASPP) to accurately localize salient objects of various scales.
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- Multi-Adversarial Faster-RCNN for Unrestricted Object Detection367
A multi-adversarial Faster-RCNN (MAF) framework for unrestricted object detection, which inherently addresses domain disparity minimization for domain adaptation in feature representation and improves the domain adaptability.
- Transfer Adaptation Learning: A Decade Survey305
The survey helps researchers rapidly but comprehensively understand and identify the research foundation, research status, theoretical limitations, future challenges, and understudied issues to be broken in the field toward generalizable representation in open-world scenarios.
- A Single Stream Network for Robust and Real-Time RGB-D Salient Object Detection258
A single stream network to directly use the depth map to guide early fusion and middle fusion between RGB and depth, which saves the feature encoder of the depth stream and achieves a lightweight and real-time model.
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- Dynamic Weighted Learning for Unsupervised Domain Adaptation192
Dynamic Weighted Learning is proposed to avoid the discriminability vanishing problem caused by excessive alignment learning and domain misalignment problem causedby excessive discriminant learning and has an excellent performance in several benchmark datasets.
- Domain Adaptive Object Detection via Asymmetric Tri-Way Faster-RCNN168
This work proposes an asymmetric tri-way Faster-RCNN (ATF) for domain adaptive object detection, which has two distinct merits: 1) A ancillary net supervised by source label is deployed to learn anCillary target features and simultaneously preserve the discrimination of source domain, which enhances the structural discrimination of domain alignment.
- Classification of multiple indoor air contaminants by an electronic nose and a hybrid support vector machine166
Experimental results have demonstrated that the HSVM model outperforms other classifiers in general, and HSVM classifier preliminarily shows its superiority in solution to discrimination in various electronic nose applications.
- Evolutionary Cost-Sensitive Extreme Learning Machine160
This paper proposes an evolutionary cost-sensitive ELM, which to the best of the authors' knowledge, is the first proposal of ELM in evolutionary Cost-sensitive classification scenario, and well addresses the open issue of how to define the cost matrix in cost- sensitive learning tasks.
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- A Data Augmentation Approach for Sign-Language-To-Text Translation In-The-Wild128
The goal is to build a state-of-the-art system for the automatic interpretation of sign language in unpredictable video framing conditions and the set of hypotheses to build such a system are described and the list of experiments that will lead to their verification are described.
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- Manifold Criterion Guided Transfer Learning via Intermediate Domain Generation114
A novel local generative discrepancy metric-based intermediate domain generation learning called Manifold Criterion guided Transfer Learning (MCTL) is proposed in this paper and demonstrated the superiority of the proposed MC guided generative transfer method on a number of benchmark visual transfer tasks.
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- Anchor Box Optimization for Object Detection108
This paper proposes to dynamically learn the anchor shapes, which allows the anchors to automatically adapt to the data distribution and the network learning capability, and achieves significant improvement over the baseline methods on several benchmark datasets.
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- Self-adaptive Re-weighted Adversarial Domain Adaptation86
This paper proposes a novel trajectory representation learning framework Traj2SimVec that performs scalable and robust trajectory similarity computation and supports the point matching query by modeling the optimal matching relationship of trajectory points under different distance metrics.
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- Large-Scale Pre-training for Person Re-identification with Noisy Labels82
This paper develops a large-scale Pre-training frame-work utilizing Noisy Labels (PNL), which consists of three learning modules: supervised Re-ID learning, prototype-based contrastive learning, and label-guided Contrastive learning.
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- Multi-view Adversarial Discriminator: Mine the Non-causal Factors for Object Detection in Unseen Domains71
A Multi-view Adversarial Discriminator (MAD) based domain generalization model, consisting of a Spurious Correlations Generator (SCG) that increases the diversity of source domain by random augmentation and a Multi-View Domain Classifier (MVDC) that maps features to multiple latent spaces, such that the non-causal factors are removed and the domain-invariant features are purified.
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- A novel classifier ensemble for recognition of multiple indoor air contaminants by an electronic nose69
The proposed ISVMEN can obtain the best discrimination accuracy for C7H8, CO and NH3, almost 100% classification accuracy was obtained using the proposed method, and is better significantly than other methods in classification and generalization performance.
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- Unsupervised Domain Adaptation of Black-Box Source Models68
This work proposes a simple yet effective method, termed Iterative Noisy Label Learning (IterNLL), which alternates between learning improved target models from the target subset with more reliable labels and updating the noisy target labels, and performs comparably with methods of the traditional UDA setting where the labeled source data are fully available.
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- View Confusion Feature Learning for Person Re-Identification63
This paper proposes an end-to-end trainable framework, called View Confusion Feature Learning (VCFL), for person Re-ID across cameras, and extracts sift-guided features by using bag-of-words model to help supervise the training of deep networks and enhance the view invariance of features.
- Chaos based neural network optimization for concentration estimation of indoor air contaminants by an electronic nose63
Experimental results demonstrate the superiority and efficiency of the portable E-nose instrument integrated with artificial neural network optimized by chaotic sequence based optimization algorithms in real-time monitoring of air quality in dwellings.
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- VIVO: Surpassing Human Performance in Novel Object Captioning with Visual Vocabulary Pre-Training.59
The results show that the VIsual VOcabulary pre-training model can not only generate fluent image captions that describe novel objects, but also identify the locations of these objects.
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- One-Shot Learning as Instruction Data Prospector for Large Language Models56
It is shown that instruction tuning with the top 1\% of examples curated by \textsc{Nuggets} substantially outperforms conventional methods employing the entire dataset.
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- Deep object recognition across domains based on adaptive extreme learning machine56
The proposed AELM is an extension of ELM from single task to cross task learning, by introducing a new error term and Laplacian graph based manifold regularization term in objective function and is comparable and effective in single and multiple domains based recognition tasks.
- SVM and ELM: Who Wins? Object Recognition with Deep Convolutional Features from ImageNet56
The nearest neighbor, support vector machines and extreme learning machines for image classification under deep convolutional activation feature representation and state-of-the-art results are obtained by kernel ELM which outperforms SVMs in cross-domain recognition tasks.
- A novel sensor selection using pattern recognition in electronic nose56
A potential and full contribution analysis of the small sized sensor array, in detection of indoor air contaminants coupled with a kernel principal component analysis (KPCA) based linear discriminant analysis (LDA) pattern recognition technique, is presented.
- Optimal Projection Guided Transfer Hashing for Image Retrieval54
This work proposes a simple yet effective unsupervised hashing method named Optimal Projection Guided Transfer Hashing (GTH), which aims to learn domain-invariant hashing functions and seeks for the maximum likelihood estimation (MLE) solution of the error matrix between two hashing projections due to the domain gap.
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- Online Learners’ Reading Ability Detection Based on Eye-Tracking Sensors53
A computational model is established according to the empirical eye-tracking data, and applying the multi-feature regularization machine learning mechanism based on a Low-rank Constraint presents good generalization ability with an error of only 4.9% when randomly running 100 times.
- Visual Understanding via Multi-Feature Shared Learning With Global Consistency53
A newly proposed l2-norm-based multi- feature shared learning framework, which can simultaneously learn a global label matrix and multiple sub-classifiers with the labeled multi-feature data, and a group graph manifold regularizer composed of the Laplacian and Hessian graph is proposed.
- Boosting image classification with LDA-based feature combination for digital photograph management53
This paper proposes a general statistical learning method based on boosting algorithm to perform image classification for photograph annotation and management that has several advantages: small model size, computational efficiency and improved classification performance based on LDA feature combination.
- Probability Weighted Compact Feature for Domain Adaptive Retrieval52
An effective method named Probability Weighted Compact Feature Learning (PWCF), which provides inter-domain correlation guidance to promote cross-domain retrieval accuracy and learns a series of compact binary codes to improve the retrieval speed, is proposed.
- A new kernel discriminant analysis framework for electronic nose recognition51
Experimental results demonstrate that the proposed KNDA method shows the best performance with average recognition rate and total recognition rate as 94.14% and 95.06% which leads to a promising feature extraction and multi-class recognition in e-Nose.
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- Category Dictionary Guided Unsupervised Domain Adaptation for Object Detection48
A category dictionary guided (CDG) UDA model for cross-domain object detection is proposed, which learns category-specific dictionaries from the source domain to represent the candidate boxes in target domain and a residual weighted self-training paradigm is developed to implicitly align source and target domains for detection model training.
- The Biological Roles and Molecular Mechanisms of Long Non-Coding RNA MEG3 in the Hallmarks of Cancer46
Recent knowledge regarding the role of MEG3 in regulating tumor hallmarks as well as the underlying molecular mechanisms are summarized.
- Neuron Pruning-Based Discriminative Extreme Learning Machine for Pattern Classification43
The developed P-DELM can naturally distinguish the importance of different hidden neurons, which will lead to a more compact network by neuron pruning, and can achieve promising performance with fewer hidden neurons and less prediction time on several benchmark datasets.
- Subspace clustering using a low-rank constrained autoencoder43
The proposed low-rank constrained autoencoder (LRAE), forces the latent representation of the neural network to be of low rank, and the low- rank constraint is computed as a prior from the input space, substantially outperformed state-of-the-art subspace clustering methods.
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- A Novel Air Quality Early-Warning System Based on Artificial Intelligence42
The results show that the prediction model is superior to other benchmark models in pollutant concentration prediction and the evaluation model is satisfactory in air quality level reporting compared with the actual status.
- Domain class consistency based transfer learning for image classification across domains42
The essential objective of the proposed transfer learning method is to maximize the DCC metric, which is equivalently to minimize the domain-class-inconsistency (DCIC), such that domain distribution mismatch and class inseparability are well formulated and unified simultaneously.
- Entity set expansion in opinion documents41
This paper focuses on mining entities (e.g., Sony), and proposes a re-ranking method to solve the problem, and designs some generic features based on part-of-speech tags of words for learning, which thus does not need to engineer features for each specific domain.
- Multi-adversarial Faster-RCNN with Paradigm Teacher for Unrestricted Object Detection40
This work proposes a Paradigm Teacher (PT) with knowledge distillation and formulated an extensive MAF (PT-MAF), which has two new contributions: The Paradigm teacher overcomes source error collapse to improve the adaptability of the model and the Dual-Discriminator HDFA improves the marginal distribution and achieves better alignment compared to HDFA.
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- Tasks Integrated Networks: Joint Detection and Retrieval for Image Search38
An end-to-end Integrated Net is introduced, called DC-I-Net, which outperforms the state-of-the-art tasks-integrated and tasks-separated image search models and makes two new contributions: two modules are tailored to handle different tasks separately in the integrated framework, such that the task specification is guaranteed.
- Common Subspace Learning via Cross-Domain Extreme Learning Machine38
Experiments demonstrate that the proposed CdELM method significantly outperforms other compared methods on electronic nose olfaction datasets.
- EnjoyPhoto38
A vertical image search engine called EnjoyPhoto is proposed that leverages rich metadata from various photo forum web sites to meet users' requirements for enjoying high-quality photos, which is virtually impossible in traditional image search engines.
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- BP-triplet net for unsupervised domain adaptation: A Bayesian perspective35
This work proposes an effective BP-Triplet Loss for unsupervised domain adaption (UDA) from the perspective of Bayesian learning and it can self attend to the hard pairs and can be upper bounded following Ben-David s theorem.
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- Universal Domain Adaptive Object Detector23
US-DAF is proposed, namely Universal Scale-Aware Domain Adaptive Faster RCNN with Multi-Label Learning, to reduce the negative transfer effect during training while maximizing transferability as well as discriminability in both domains under a variety of scales.
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- HyperLLaVA: Dynamic Visual and Language Expert Tuning for Multimodal Large Language Models22
HyperLLaVA is introduced, which involves adaptive tuning of the projector and LLM parameters, in conjunction with a dynamic visual expert and language expert, respectively, derived from HyperNetworks, enabling dynamic projector and LLM modeling in two-stage training.
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- Review Article: Decoding the Role of PBX3 in Cancer: Molecular Mechanisms and Therapeutic Strategies–
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- ARTIFICIAL NEURAL NETWORK BASED CHAOTIC GENERATOR DESIGN FOR THE PREDICTION OF FINANCIAL TIME SERIES–
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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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