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Knowledge graph completion (KGC) aims to fill in missing entities and relations within knowledge graphs (KGs) to address their incompleteness. Most existing KGC models suffer from knowledge coverage as they are designed to operate within a single ...

Among various traffic data modeling and predicting methods, graph learning-based models attract more attention, because of their powerful representation ability for modeling spatial and temporal dependencies with graph neural networks. Despite ...

Short videos turn into an important channel for public sharing, as well as they've become a fertile ground for fake news. Fake news video detection is to judge the veracity of news based on its different modal information, such as video, audio, text, ...

3D Human pose estimation from multiple cameras with unknown calibration has received less attention than it should. The few existing data-driven solutions do not fully exploit 3D training data that are available on the market, and typically train from ...

The functions of urban regions are diverse and complex, making the accurate understanding and identifying these functions is crucial for urban planning and management. However, previous methods usually delineate urban regions based on regular network ...

Improved thyroid nodule risk stratification from ultrasound (US) can mitigate overdiagnosis and unnecessary biopsies. Previous studies often train deep learning models using manually selected single US frames; these approaches deviate from ...

We present a novel method for 3D human body reconstruction with multi-view images from calibration-free cameras by multi-view fusion with explicit visibility modelling. Existing multi-view methods usually establish geometric constraints by using accurate ...

Multiview subspace clustering (MVSC) has attracted an increasing amount of attention in recent years. Most existing MVSC methods first collect complementary information from different views and consequently derive a consensus reconstruction coefficient ...

Multiview High Efficiency Video Coding (MV-HEVC) is an HEVC extension focused on efficiently coding spatially related images, such as a left eye and right eye views of 3D stereoscopic content. MV-HEVC was released in the second version of HEVC back in ...

In this work, we aim to advance the traditional approach to 3D object classification for its lighter computation and memory costs than the deep learning based approach. Specifically, we propose a novel algorithm that uses multiple handcrafted descriptors ...

Contrastive learning (CL) has exploded in popularity due to its ability to learn effective representations using vast quantities of unlabelled data across multiple domains. CL underlies some of the most impressive applications of generative AI for the ...

Alzheimer's disease (AD) is a progressive neurodegenerative brain disorder, the leading cause of dementia, characterised by memory loss and cognitive decline affecting daily life. Early detection is crucial for effective treatment. 18F-FDG-PET is the ...

Thanks to the high-quality reconstruction and photorealistic rendering, the Neural Radiance Field (NeRF) has garnered extensive attention and has been continuously improved. Despite its high visual quality, the prohibitive training time limits its ...

Recent works on Scene Graph Generation (SGG) have been concentrating on solving the problem of long-tailed distribution. While these methods are making significant improvements on the tail predicate categories, they sacrifice the performance of the head ...

Multimedia recommendation has received much attention in recent years. It models user preferences based on both behavior information and item multimodal information. Though current GCN-based methods achieve notable success, they suffer from two ...

Bird's-Eye-View (BEV) based 3D visual perception, which formulates a unified space for multi-view representation, has received wide attention in autonomous driving due to its scalability for downstream tasks. However, view transform in transformer-based ...

Multi-view (representation) learning derives an entity's representation from its multiple observable views to facilitate various downstream tasks. The most challenging topic is how to model unobserved entities and their relationships to specific views. ...

Robust object grasping in cluttered scenes is vital to all robotic prehensile manipulation. In this paper, we present the GraspNet-1Billion benchmark that contains rich real-world captured cluttered scenarios and abundant annotations. This benchmark aims ...

Recent research introduces deep learning algorithms such as recurrent neural networks (RNNs) to predict the next activity, one of the most challenging tasks in predictive business process monitoring. However, the RNN-based models use only the last ...

Neural Radiance Fields (NeRF) are a rapidly growing area of research with wide-ranging applications in computer vision, graphics, robotics, and more. In order to streamline the development and deployment of NeRF research, we propose a modular PyTorch ...