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Large-scale text-to-image models enable a wide range of image editing techniques, using text prompts or even spatial controls. However, applying these editing methods to multi-view images depicting a single scene leads to 3D-inconsistent results. In this ...

Real-world applications frequently involve multiple data modalities in the same samples, which are regarded as multi-view data. Multi-view clustering has been studied extensively in recent years to demonstrate embedded heterogeneity. However, ...

• We proposed a co-regularized Optimal High-Order Graph Embedding Method Co-MSE.
• Optimal embedding representation for multi-view data can be obtained in Co-MSE.
• Co-MSE is very efficient and can converge in a few iterations.
• ...

In the majority of existing multi-view clustering methods, the prerequisite is that the data have the correct cross-view correspondence. However, this strong assumption may not always hold in real-world applications, giving rise to the so-called ...

• We propose a clustering solution for the view-shuffled problem with an alignment ratio ranging from 0 to 1.
• The data alignment and partition are linked seamlessly, leading to improved performance.
• The convergence of the ...

Multi-view unsupervised feature selection (MUFS) has been proven to be an efficient dimensionality reduction technique for multi-view data. Existing methods have two main challenges: (1) The consistency information from different views is not ...

Existing multi-view multi-person 3D human pose estimation often uses explicit cross-matching methods or implicit 3D voxel and projection to estimate joint positions. Explicit methods are limited by accuracy, while implicit ones affect real-time ...

Compared to the prediction for individuals, motion prediction in 3D scenes remains a challenging task, often requiring guidance on both historical motion and the surrounding environment. However, the issue of how to effectively introduce scene ...

Recently, multi-view high dimensional data obtained from diverse domains or various feature extractors has drawn great attention due to its reflection of different properties or distributions. In this paper, we propose a novel unsupervised multi-...

• In order to avoid the influence of dimensional curse and redundant features in the original space, we use dimension reduction technology to process high-dimensional multi-view data.
• We use the σ-norm as an adaptive loss minimization, ...

Traditional methods for predicting drug–target interactions (DTIs) have significant room for improvement in terms of time period and monetary overhead. At present, machine learning-based approaches are commonly used in the drug discovery field. ...

• The MvG-NRLMF model for drug–target interaction identification is proposed.
• Uses a multi-perspective multinuclear space to model drug–target relationships.
• Laplace matrix and graph regularization methods are combined.
• The ...

In recent years, numerous studies have established individual or combined imaging modalities as fundamental approaches for the early diagnosis of Alzheimer’s disease (AD). Additionally, cortical surface analysis has proven effective in brain ...$\mathrm{}$$\mathrm{}$

Multi-view crowd localization predicts the ground locations of all people in the scene. Typical methods usually estimate the crowd density maps on the ground plane first, and then obtain the crowd locations. However, existing methods’ performances ...

We propose a 3D multi-object tracking (MOT) solution using only 2D detections from monocular cameras, which automatically initiates/terminates tracks as well as resolves track appearance–reappearance and occlusions. Moreover, this approach does ...

• Novel 3D multi-object tracking models with re-identification features.
• A filter that performs 3D tracking by fusing multi-view 2D camera detections.
• Our Method automatically initializes/terminates, re-identifies, and handles ...

The feature selection problem in multi-view data has garnered widespread attention and research in recent years, leading to the development of numerous feature selection algorithms tailored for multi-view data. However, existing methods often ...

• A novel UAV-based method for efficient SCR monitoring.
• Green-red-red edge band related spectral indices are optimal features.
• Multi-view spectral measurements outperform single-view spectral measurements.
• 15 degree view has the ...

Southern corn rust (SCR) is a significant foliar disease, which can result in substantial corn yield losses. Unmanned aerial vehicle (UAV)-based optical remote sensing presents a promising method for efficiently monitoring SCR in field ...

Signed bipartite graphs differ from traditional graphs, which include two sets of nodes and signed edges. With the development of information technology, sign prediction in bipartite graphs has become a hotspot in both research and industrial ...

Despite the meaningful advancements in graph-based multi-view clustering methods, several challenges persist. Firstly, these methods often suffer from high computational costs, limiting their application to large-scale datasets. Secondly, direct ...

To address 3D object retrieval, substantial efforts have been made to generate highly discriminative descriptors for 3D objects represented by a single modality, such as voxels, point clouds, or multiview images. It is promising to leverage ...

In the field of skin disease diagnosis based on Convolutional Neural Networks (CNNs), there are currently two challenges. Firstly, there is a significant amount of label-independent information present in skin disease images. This information ...

• An effective and additive multi-view skin disease diagnosis model is proposed.
• Redundant information perturbation and optimal view combination are considered.
• Multi-view compression is proposed to alleviate redundant information ...

Development of machine learning (ML) systems differs from traditional approaches. The probabilistic nature of ML leads to a more experimentative development approach, which often results in a disparity between the quality of ML models with other ...

For the problems of irrelevant frames and high model complexity in action recognition, we propose a Spatial–Temporal Hypergraph based on Dual-Stage Attention Network (STHG-DAN) for multi-view data lightweight action recognition. It includes two ...

• Dual-stage attention network: It includes two stages: Temporal Attention Mechanism based on Trainable Threshold (TAM-TT) and Hypergraph Convolution based on Dynamic Spatial-Temporal Attention Mechanism (HG-DSTAM).
• Salient region: HG-...

Recently, 3D shape understanding has achieved significant progress due to the advances of deep learning models on various data formats like images, voxels, and point clouds. Among them, point clouds and multi-view images are two complementary ...

• We propose a Locality-Aware Fusion (LAF) module to fuse point clouds and images.
• Based on LAF, we construct Locality-Aware Point-View Fusion Transformer.
• Significant improvements over state-of-the-arts on 3D shape datasets are ...