Sparse multi-view image clustering with complete similarity information
Multi-view image clustering aims to efficiently divide the collection of images by studying the characteristics of different views. Many studies performed Laplacian dimensionality reduction on the original image to avoid noise interference in ...
Middle fusion and multi-stage, multi-form prompts for robust RGB-T tracking
RGB-T tracking, a vital downstream task of object tracking, has made remarkable progress in recent years. Yet, it remains hindered by two major challenges: (1) the trade-off between performance and efficiency; (2) the scarcity of training data. ...
The use of reinforcement learning algorithms in object tracking: A systematic literature review
Object tracking is a computer vision task that aims to locate and continuously follow the movement of an object in video frames, given an initial annotation. Despite its importance, this task can prove to be challenging due to factors such as ...
Visual fire detection using deep learning: A survey
Visual Fire Detection (VFD), through the rapid and accurate identification of smoke and flame in images and videos, is crucial for early fire warning and reducing fire hazards. In recent years, the introduction of deep learning has significantly ...
SPNet: Semantic preserving network with semantic constraint and non-semantic calibration for color constancy
Recent methods introduce semantics obtained from pre-trained classification models into color constancy to guide the model in learning object-color mapping, thereby improving the illumination estimation ability. However, the task discrepancy ...
Depression risk recognition based on gait: A benchmark
Recently, depression recognition has received considerable attention. Due to easy acquisition at a distance, gait-based depression recognition can be a useful tool for auxiliary diagnosis and self-help depression risk assessment. Most existing ...
Quasi-synchronization of neural networks via non-fragile impulsive control: Multi-layer and memristor-based
In this paper, the quasi-synchronization for memristor-based multi-layer neural networks is solved, where each layer possesses a unique topology. The focus is on the incorporation of proportional delay, which represents an exceptional unbounded ...
Efficient tick-shape networks of full-residual point-depth-point blocks for image classification
Light-weight convolutional neural networks (CNNs) are crucial for deploying computer vision applications in mobile devices thanks to their compact models, small computational complexity, and energy efficiency. However, such models ordinarily have ...
Structural Transformer with Region Strip Attention for Video Object Segmentation
Memory-based methods in semi-supervised video object segmentation (VOS) achieve competitive performance by performing feature similarity between the current frame and memory frames. However, this operation involves two challenges: 1) instances of ...
Fine-grained and coarse-grained contrastive learning for text classification
Pre-trained language models based on contrastive learning have shown to be effective in text classification. Although its great success, contrastive learning still has shortcomings. First, most of the existing contrastive learning methods neglect ...
Improvement of Waegeman–Baets–Boullart algorithms for ordered multi-class ROC analysis
To accommodate multi-class scenarios, area under the receiver operating characteristic (ROC) curve (AUC) has been extended to volume under the ROC hyper-surface (VUHS) to measure the overall power of a model to classify objects belonging to ...
Highlights
- Waegeman et al. developed three fast algorithms, including WBBA0, WBBA1 and WBBA2.
- WBBA1 (WBBA2) is the state-of-the-art methods for estimating (co-)variance of VUHS(s).
- WBBA1 and WBBA2 are only asymptotically unbiased.
- Derived ...
Data-distribution-informed Nyström approximation for structured data using vector quantization-based landmark determination
We present an effective method for supervised landmark selection in sparse Nyström approximations of kernel matrices for structured data. Our approach transforms structured non-vectorial input data, like graphs or text, into a dissimilarity ...
Highlights
- We introduce a efficient variant for Nyström approximation of kernel Gram matrices, if the subsequent task is a classification problem.
- In that case, the class information can be used to improve the standard landmark selection scheme.
Feature selection considering feature relevance, redundancy and interactivity for neighbourhood decision systems
Feature selection is an effective method to simplify data analysis and obtain key features, which improves the accuracy and generalization ability of classifiers. Neighbourhood rough set is a typical granular computing model that enables data ...
Deep learning for 3D human pose estimation and mesh recovery: A survey
3D human pose estimation and mesh recovery have attracted widespread research interest in many areas, such as computer vision, autonomous driving, and robotics. Deep learning on 3D human pose estimation and mesh recovery has recently thrived, ...
Towards federated feature selection: Logarithmic division for resource-conscious methods
Feature selection is a popular preprocessing step to reduce the dimensionality of the data while preserving the important information. In this paper, we propose an efficient and green feature selection method based on information theory, with the ...
Highlights
- Implementation of green feature selection methods based on information theory.
- Study of logarithmic division to reduce energy and memory consumption.
- Federated Mutual Information calculation enables IoT environments data privacy.
