The historical interaction sequences of users play a crucial role in training recommender systems that can accurately predict user preferences. However, due to the arbitrariness of user behaviors, the presence of noise in these sequences poses a ...

Federated multi-task learning (FMTL) has emerged as a promising framework for learning multiple tasks simultaneously with client-aware personalized models. While the majority of studies have focused on dealing with the non-independent and ...

Recommendation has been widely used in business scenarios to provide users with personalized and accurate item lists by efficiently analyzing complex user-item interactions. However, existing recommendation methods have significant shortcomings in ...

Point cloud analysis is challenging because of the unordered and irregular data structure of point clouds. To describe geometric information in point clouds, existing methods mainly use convolution, graph, and attention operations to construct ...

Deep neural networks (DNNs) are vulnerable to elaborately crafted and imperceptible adversarial perturbations. With the continuous development of adversarial attack methods, existing defense algorithms can no longer defend against them ...$${\mathcal{}}_{}$$

Inductive knowledge graph embedding (KGE) aims to embed unseen entities in emerging knowledge graphs (KGs). The major recent studies of inductive KGE embed unseen entities by aggregating information from their neighboring entities and relations ...

Travel time estimation (TTE) is a fundamental task to build intelligent transportation systems. However, most existing TTE solutions design models upon simple homogeneous graphs and ignore the heterogeneity of traffic networks, where, e.g., main ...

Multi-task learning (MTL) can boost the performance of individual tasks by mutual learning among multiple related tasks. However, when these tasks assume diverse complexities, their corresponding losses involved in the MTL objective inevitably ...

K-nearest neighbor (KNN) is one of the most fundamental methods for unsupervised outlier detection because of its various advantages, e.g., ease of use and relatively high accuracy. Currently, most data analytic tasks need to deal with high-...

This paper presents ScenePalette, a modeling tool that allows users to “draw” 3D scenes interactively by placing objects on a canvas based on their contextual relationship. ScenePalette is inspired by an important intuition which was often ignored ...

Coincidence detection of two curves or two surfaces has wide application in computer-aided design (CAD) and computer-aided geometric design (CAGD). Proper reparameterization is the most complicated part in the detection. This paper presents and ...

This paper presents an original investigation into the domain of violence detection in videos, introducing an innovative approach tailored to the unique challenges of a federated learning environment. The study encompasses a comprehensive ...

As the virtual reality (VR) technology strives to provide immersive and natural user experiences, the challenge of aligning vast virtual environments with limited physical spaces remains significant. This survey comprehensively explores the ...

In the domain of point cloud registration, the coarse-to-fine feature matching paradigm has received significant attention due to its impressive performance. This paradigm involves a two-step process: first, the extraction of multilevel features, ...

Face anti-spoofing aims at detecting whether the input is a real photo of a user (living) or a fake (spoofing) image. As new types of attacks keep emerging, the detection of unknown attacks, known as Zero-Shot Face Anti-Spoofing (ZSFA), has become ...

Knowledge distillation is often used for model compression and has achieved a great breakthrough in image classification, but there still remains scope for improvement in object detection, especially for knowledge extraction of small objects. The ...

Image-text retrieval aims to capture the semantic correspondence between images and texts, which serves as a foundation and crucial component in multi-modal recommendations, search systems, and online shopping. Existing mainstream methods ...

The glinty details from complex microstructures significantly enhance rendering realism. However, the previous methods use high-resolution normal maps to define each micro-geometry, which requires huge memory overhead. This paper observes that ...

The springing up of large language models (LLMs) has shifted the community from single-task-orientated natural language processing (NLP) research to a holistic end-to-end multi-task learning paradigm. Along this line of research endeavors in the ...

Neural network models for audio tasks, such as automatic speech recognition (ASR) and acoustic scene classification (ASC), are susceptible to noise contamination for real-life applications. To improve audio quality, an enhancement module, which ...