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Chapter and Conference Paper
Optimizing 3D Object Detection with Data Importance-Based Loss Reweighting
With the advancement of AI technology, deep learning-based intelligent driving assistance systems have seen substantial growth. However, 3D object detection remains a significant challenge due to LiDAR’s chara...
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Chapter and Conference Paper
Personalized EDM Subject Generation via Co-factored User-Subject Embedding
This paper introduces the Co-Factored User-Subject Embedding based Personalized EDM Subject Generation Framework (COUPES), a model for creating personalized Electronic Direct Mail (EDM) subjects. COUPES adapts...
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Chapter and Conference Paper
Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report
Video super-resolution is one of the most popular tasks on mobile devices, being widely used for an automatic improvement of low-bitrate and low-resolution video streams. While numerous solutions have been pro...
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Chapter and Conference Paper
Social-SSL: Self-supervised Cross-Sequence Representation Learning Based on Transformers for Multi-agent Trajectory Prediction
Earlier trajectory prediction approaches focus on ways of capturing sequential structures among pedestrians by using recurrent networks, which is known to have some limitations in capturing long sequence struc...
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Chapter and Conference Paper
Improving Entity Disambiguation Using Knowledge Graph Regularization
Entity disambiguation plays the role on bridging between words of interest from an input text document and unique entities in a target Knowledge Base (KB). In this study, to address the challenges of global en...
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Chapter and Conference Paper
Character-Preserving Coherent Story Visualization
Story visualization aims at generating a sequence of images to narrate each sentence in a multi-sentence story. Different from video generation that focuses on maintaining the continuity of generated images (f...
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Chapter and Conference Paper
Quality-Aware Streaming Network Embedding with Memory Refreshing
Static network embedding has been widely studied to convert sparse structure information into a dense latent space. However, the majority of real networks are continuously evolving, and deriving the whole embe...
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Chapter and Conference Paper
Maximizing Social Influence on Target Users
Influence maximization has attracted a considerable amount of research work due to the explosive growth in online social networks. Existing studies of influence maximization on social networks aim at deriving ...
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Article
Distributed and scalable sequential pattern mining through stream processing
Scalability is a primary issue in existing sequential pattern mining algorithms for dealing with a large amount of data. Previous work, namely sequential pattern mining on the cloud (SPAMC), has already addres...
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Chapter and Conference Paper
Scale-Adaptive Group Optimization for Social Activity Planning
Studies have shown that each person is more inclined to enjoy a group activity when 1) she is interested in the activity, and 2) many friends with the same interest join it as well. Nevertheless, even with the...
