Search Results

The non-parallel disease progression and curative effect are the difficulties of clinical diagnosis and treatment decisions. Experts (doctors) constantly summarize these non-parallel phenomena for more accurate diagnosis and treatment. In order ...

• A novel approach to investigate the consistency of multi-modal data-driven clinical decision making.
• Using DRSA to explore clinical non-parallel decision-making problems.
• Explore consistent decision-making methods in clinical non-...

Disease assessment involves two stages of decision-making process. The first process is to infer the development trend of the disease through the existing judgment conditions, which is to make a positive estimation (prediction) decision. The ...

• Three-way decision-making and semi-supervised learning integrated model was constructed.
• The multi-modal decision information method was discussed.
• Automatic update strategy of thresholds and loss function of three-way decision ...

Multi-modal tracking has garnered significant attention due to its wide range of potential applications. Existing multi-modal tracking approaches typically merge data from different visual modalities on top of RGB tracking. However, focusing ...

• Visual-Language Interaction Prompt Manager is proposed.
• Multi-modal Image Description Co-Generation Module is introduced.
• Multi-modal Visual Tracking Based on Textual Generation method is designed.

• A multi-task multi-objective evolutionary network is proposed.
• The framework combines two tasks by effective high-frequency information sharing.
• Multi-task learning is modeled as a deep MOEAD to obtain trade-off solutions.
• The ...

Multi-task learning has commonly been used and performed well at joint visual perception tasks. Hyperspectral pansharpening (HP) and hyperspectral classification (HC) tasks extract high-frequency information to enhance edges and classify samples, ...

LiDAR and camera are two common vision sensors used in the real world, producing complementary point cloud and image data. While multimodal data has previously been found mostly in 3D detection and tracking, we aim to study large-scale semantic ...

• We revisit the key factor of LiDAR-camera fusion, namely the soft joint mechanism.
• We develop an attention-based multimodal fusion in point cloud segmentation.
• We build multi-scale pairwise inputs and interact using the dual-stream ...

• An effective screening algorithm is derived by extracting angle measurements.
• An exclusion strategy of association results is proposed by building statistics.
• Angle association is calculated by least square to get coordinates of ...

Multi-target tracking among different types of sensors is facing great challenge in fully utilizing various types of measurements. To this end, this paper presents a measurement fusion algorithm of single active and multi-passive sensors (SAMPS) ...

We present a deep learning-based method for Visible-Infrared person Re-Identification (VI-ReID). The major contribution lies in the incorporation of co-segmentation into a multi-task learning framework for VI-ReID, where the co-segmentation ...

• Using the co-segmentation to assist the VI-ReID in a multi-task learning framework.
• Co-segmenting the same identity from a set of input images with different modalities.
• Providing theoretical comparisons between our proposed model ...

Incomplete multi-view clustering is pivotal in machine learning because complex systems are inherently difficult to be fully observed and therefore pose a great challenge to revealing the mechanisms and structure of underlying systems. Current ...

• UGCF integrates data recovery, feature learning, and contrastive denoising for IMC.
• UGCF introduces a graph contrastive constraint, ensuring the compactness of features.
• UGCF removes the heterogeneity and noise of views by ...

In the realm of addressing consensus challenges within group decision-making (GDM) that encompass individual preferences, this article presents an innovative approach. This article introduces a three-way group consensus methodology grounded in ...

• A method of deriving the weights of experts using their own hesitancy on evaluate information is proposed.
• A new aggregation formula of PLTSs based on the experts’ weights is constructed.
• A consistency improvement model that ...

The co-medication strategy of Chinese Materia Medica (CMM) and western medicines under the holistic integrative medicine (HIM) model offers the advantages of increased efficacy and reduced toxicity, particularly in clinical practice for treating ...

• A speaker-listener label propagation algorithm based on semantic attribute features is proposed.
• A medicine complex network is constructed to fuse multi-source information.
• A new drug combination identification strategy is ...

Super-resolution (SR) is an essential class of low-level vision tasks, which aims to improve the resolution of images or videos in computer vision. In recent years, significant progress has been made in image and video super-resolution techniques ...

• This work is the first systematic review on arbitrary scale super-resolution (SR).
• Two novel taxonomies for arbitrary scale SR methods are proposed.
• The advantages and limitations of each class of methods are analyzed.
• The ...

The clinical diagnosis decision-making process of integrated traditional Chinese medicine and Western medicine is essentially a type of group decision-making (GDM) problem, which is transparently characterized by the multiformity of data types ...

• Incomplete multi-granularity diversified compound decision systems are presented.
• A non-additive three-way decision (TWD) model is constructed.
• The TWD model and preference-approval structures are integrated.
• A new group ...

Fusion and co-learning are major challenges in multimodal sentiment analysis. Most existing methods either ignore the basic relationships among modalities or fail to maximize their potential correlations. They also do not leverage the knowledge ...

• A multimodal sentiment analysis model with correlation mining & multi-task learning.
• A multi-level correlation mining framework to discover the correlation information.
• A self-supervised label generation module to generate unimodal ...

Decision makers (DMs) may exhibit non-cooperative behaviors such as deviating from recommendations or making only minor modifications in consensus-based group decision-making (GDM) problems. These non-cooperative behaviors will lead to high intra-...

• Build a budget-constrained consensus framework with minimum group conflict.
• Manage non-cooperative behaviors by the minimum conflict consensus models.
• Obtain the budget constraint by the non-linear optimization models.
• Conflict ...

The multispectral (MS) and the panchromatic (PAN) images represent complementary and synergistic spatial spectral information, how to make optimal use of the advantages of them has become a hot research topic. This paper proposes a ...

• A subspace similar recombination module is proposed.
• The selectable self-...

Although extensive researches have carried out on visible and infrared images fusion, quality assessment of the fused image is still challenging due to the absence of the reference image. In this paper, a subjective benchmark dataset and a semi-...

• A subjective benchmark dataset called Visible and Infrared fuSed qualiTy Assessment is built.
• The subjective fused image quality assessment specification is formulated.
• A new Semantic Relation Transformer is proposed for fused ...

Host cardinality estimation is one crucial task in network traffic measurement. Super host is the host that exhibits anomalies in host cardinality and it is usually related to network abnormal events. Therefore, accurate host ...

• A memory efficient and reversible sketch named ExtendedSketch+.
• A quick and ...

The Session-based Recommendation (SBR) system aims to forecast anonymous users’ short-term decisions. Many prior research have demonstrated that using Graph Neural Networks (GNN) and Recurrent Neural Networks (RNN) to solve SBR tasks can lead to ...

• Combining graph neural networks and transformer layers efficiency.
• Graph neural network can learn graph-level item representation.
• Transformer layer is designed to learn sequential-level item representation.
• Fusion the multi-...

When imaging at a long distance by ordinary visible cameras, the wavelength of visible light is easily interfered by fog or atmospheric effects, resulting in blurry or lost details in RGB images. However, NIR cameras are robust to the ...

• We propose long range imaging using multispectral fusion of RGB and NIR images, called LRINet.

Visible-infrared cross-modality person re-identification (VI-ReID) is currently a prevalent but challenging research topic in computer vision, since it can remedy the poor performance of existing single-modality ReID models under ...

Display Omitted

• Providing a comprehensive and detailed review for cross-modality person re-identification.