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Traditional visual Simultaneous Localization and Mapping (SLAM) methods based on point features are often limited by strong static assumptions and texture information, resulting in inaccurate camera pose estimation and object localization. To ...

• Semantic vSLAM fusing point, line and plane improves perception and camera pose estimation.
• Dense, semi-dense and sparse modes for flexible mapping in dynamic scenes.
• 6DOF pose estimation of objects for enriched semantic ...

With the evolution of human–robot collaboration in advanced manufacturing, multisensor integration has increasingly become a critical component for ensuring safety during human–robot interactions. Given the disparities in range scales, densities, ...

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• A method for sensor fusion in human–robot environment is proposed.
• A coarse localization algorithm is proposed to address point cloud scale differences.
• An improved FPFH algorithm based on overlap ratio for coarse registration.

In the field of multi-view learning (MVL), it is crucial to extract both common (consistent) and unique (complementary) information across different views. While the focus has traditionally been on acquiring common information, there has been a ...

• We present a novel divergence-guided framework for multi-view learning.
• Generalized divergence ensures its efficiency and scalability with multi-view data.
• Evaluation on synthetic data confirms disentanglement of common/unique ...

A large number of unanswered products-related questions appear in the E-commerce platforms, necessitating the deployment of question-answering models to automatically provide precise responses for the user. However, the substantial absence of ...

Short-text Expansion plays a significant role in enhancing the quality, diversity, and practicality of Short-text, helping users to more comprehensively understand the content expressed in the Short-text. In this paper, we aim to enhance the ...

Amidst advancements in feature extraction techniques, research on multi-view multi-label classifications has attracted widespread interest in recent years. However, real-world scenarios often pose a challenge where the completeness of multiple ...

• A HCC prognostic prediction model called NecroGlobalGCN was proposed, which locates micronecrotic areas on WSI and learns the associations between micronecrosis features and patient survival during training.
• By constructing a 2-...

Hepatocellular carcinoma (HCC) ranks fourth in cancer mortality, underscoring the importance of accurate prognostic predictions to improve postoperative survival rates in patients. Although micronecrosis has been shown to ...

The rise of sophisticated malware poses a grave threat to computer security, challenging traditional detection methods. Traditional malware detection techniques, which primarily rely on feature engineering and defining rules to identify malware, ...

Mainstream person search models aim to jointly optimize person detection and re-identification (ReID) in a one-step manner. Despite notable progress, existing one-step person search models still face three major challenges in extracting ...

• Adaptive Shift and Task Decoupling method extracts highly discriminative features.
• Scale-Aware Transformer effectively handles scale and occlusion variations.
• Task Decoupling Mechanism separates detection and re-identification ...

• Achieved automatic planning for multi-layer, multi-axis, interference-free cutting paths in ASHM of composite enclosed cavity.
• The recognition criteria for cavity-type hybrid machining features have been defined.
• The automatic ...

Additive and subtractive hybrid manufacturing (ASHM) refers to the hybrid manufacturing process where in-situ subtractive machining (SM) is introduced during additive manufacturing (AM). Its process characteristics dictate the necessity of ...

• A method captures domain distribution information for cross-modality segmentation.
• The method uses a style transfer unit with CycleGAN and a dual-normalization network.
• This work uses B-mode and elastographic ultrasound as the ...

Elastographic ultrasound (EUS) evaluates lesion stiffness, providing valuable diagnostic information for various diseases. However, accessibility, cost, and visual clarity of tissue structures are limitations of EUS compared to conventional B-...

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To overcome the challenges of complex time-varying satellite channels and severe inter-user interference in non-orthogonal multiple access (NOMA), rational power allocation and accurate multi-user joint detection methods are essential. In this paper, a ...

The blockchain platforms (BPs) have emerged as important tools for improving enterprise data security. However, the selection of a suitable BP for is a challenging issue for enterprises. In this paper, a novel heterogenous multi-criteria decision-...

Acquiring reviewers for academic submissions is a challenging recommendation scenario. Recent graph learning-driven models have made remarkable progress in the field of recommendation, but their performance in the academic reviewer recommendation task may ...

Dataset bias, i.e., the over-reliance on dataset-specific literal heuristics, is getting increasing attention for its detrimental effect on the generalization ability of NLU models. Existing works focus on eliminating dataset bias by down-...

The latest progress in novel view synthesis can be attributed to the Neural Radiance Field (NeRF), which requires densely sampled images with precise camera poses. However, collecting dense input images for a NeRF with accurate camera poses is highly ...

The recognition of named entities in visually-rich documents (VrD-NER) plays a critical role in various real-world scenarios and applications. However, the research in VrD-NER faces three major challenges: complex document layouts, incorrect reading ...

The limited data availability and the low signal-to-noise ratio of fMRI signals lead to the challenging task of fMRI-to-image retrieval. State-of-the-art MindEye remarkably improves fMRI-to-image retrieval performance by leveraging a large model, i.e., a ...

Interpretable and robust medical diagnoses are essential traits for practicing clinicians. Most computer-augmented diagnostic systems suffer from three major problems: non-interpretability, limited modality analysis, and narrow focus. Existing frameworks ...

We address the real problem of safe, robust, adaptive resource oversubscription in uncertain environments with our proposed novel technique of chance-constrained imitation learning. Our objective is to enhance resource efficiency while ensuring safety ...