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- Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation223
A graph-based global reasoning unit is introduced to strengthen the classification network’s ability to capture global relations among disjoint and distant regions and a potential object mining module is proposed to reduce the false-negative rate in pseudo labels.
- Minimum cost attribute reduction in decision-theoretic rough set models210
A new definition of attribute reduct for decision-theoretic rough set models is provided and a heuristic approach, a genetic approach and a simulated annealing approach to the new problem are proposed.
- Jo-SRC: A Contrastive Approach for Combating Noisy Labels206
A noise-robust approach named Jo-SRC (Joint Sample Selection and Model Regularization based on Consistency) that trains the network in a contrastive learning manner to advance the model generalization performance by introducing consistency regularization.
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- On an optimization representation of decision-theoretic rough set model147
An optimization representation of decision-theoretic rough set model is proposed by considering the minimization of the decision cost and two significant inferences can be drawn from the solution of the optimization problem.
- A theorem on the uncorrelated optimal discriminant vectors109
It is proved that the classical optimal discriminant vectors are equivalent to UODV, which can be used to extract (L−1) uncorrelated discriminant features for L-class problems without losing any discriminant information in the meaning of Fisher discriminant criterion function.
- Hierarchical initialization approach for K-Means clustering98
The experimental results show that the proposed hierarchical initialization approach to the K-Means clustering problem needs less iteration time compared with existing approaches and has better performance in terms of convergence speed and ability to reduce the impact of noises.
- Feature Affinity-Based Pseudo Labeling for Semi-Supervised Person Re-Identification89
This paper proposes a novel feature affinity-based pseudo labeling method with two possible label encodings, and is the first study that employs pseudo-labeling by measuring the affinity of unlabeled samples with the underlying clusters of labeled data samples using the intermediate feature representations from deep networks.
- Improving protein-ATP binding residues prediction by boosting SVMs with random under-sampling81
A modified AdaBoost ensemble scheme based on random under-sampling is developed and a method for objectively reporting evaluation results under the imbalanced learning scenario is discussed, showing that the proposed method achieves higher prediction accuracy.
- WLBP: Weber local binary pattern for local image description77
A local descriptor, called Weber Local Binary Pattern (WLBP 1), which effectively combines the advantages of WLD and LBP, which is evaluated on the face recognition problem under different challenges and demonstrates that WLBP is a powerful texture descriptor.
- TargetATPsite: A template‐free method for ATP‐binding sites prediction with residue evolution image sparse representation and classifier ensemble76
A new sequence‐based template‐free predictor (TargetATPsite) to identify the Adenosine‐5′‐triphosphate (ATP) binding sites with machine‐learning approaches is reported, which is effective for dealing with imbalance phenomenon between the positive and negative training samples.
- Web-Supervised Network with Softly Update-Drop Training for Fine-Grained Visual Classification66
A novel approach to remove irrelevant samples from the real-world web images during training, and only utilize useful images for updating the networks is proposed, which can alleviate the harmful effects caused by irrelevant noisy web images to achieve better performance.
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- Semantically Meaningful Class Prototype Learning for One-Shot Image Segmentation61
This paper proposes to leverage the multi-class label information during the episodic training to encourage the network to generate more semantically meaningful features for each category and proposes a fused prototype guidance branch for the segmentation of the query image.
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- Robust gait recognition using hybrid descriptors based on Skeleton Gait Energy Image55
A new model-based representation, Skeleton Gait Energy Image (SGEI), has been proposed in this paper and the experimental performances indicate that the proposed methods are more robust to the cloth changes, and contribute to increasing the robustness of gait recognition in the unconstrained environments with view variances and clothes changes.
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- Modified particle filter-based infrared pedestrian tracking54
A modified particle filter algorithm that combines intensity and edge cues is proposed that can track the infrared pedestrian more effectively and reliably than the traditional particle filter algorithms.
- PLDA modeling in i-vector and supervector space for speaker verification51
The probabilistic linear discriminant analysis (PLDA) is employed to handle speaker and session variability for speaker verification task and the Gaussianized rank-norm is introduced, as an alternative to whitening, for feature normalization prior to PLDA modeling.
- Feature mask network for person re-identification50
A Feature Mask Network (FMN) is proposed that takes advantage of ResNet high-level features to predict a feature map mask and then imposes it on the low- level features to dynamically re-weight different object parts for a complementary feature representation.
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- Dispersion based Clustering for Unsupervised Person Re-identification.44
A Dispersion based Clustering (DBC) approach which performs better at discovering the underlying data patterns and can automatically prioritize standalone data points and prevents poor clustering.
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 2026-10-15. 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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