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Enhancing Automated Vending Machine Product Recognition Through Depth-Guided Regression Refinement
Deep neural network advancements have led to significant progress in industrial applications, particularly in product recognition for smart automated vending machines (AVMs). This area has seen increased market demand as a fundamental part of automatedretail. However, the existing works possess critical gaps: 1) densely placed and obscured objects in AVMs lead to inaccurate product recognition res...Show More
Rethinking Label Assignment and Sampling Strategies for Two-Stage Oriented Object Detection
Oriented object detection seeks to determine both the position and orientation of objects, yet angle periodicity often limits performance. To solve this issue, we rethink label assignment and sampling strategies and propose a pair of orientation-aware assigner and sampler (OAS) for a two-stage detector. The orientation-aware assigner (OA) incorporates angle and location to improve positive and neg...Show More
Enhancing Weather Model: A Meteorological Incremental Learning Method with Long-term and Short-term Asynchronous Updating Strategy
2024 International Joint Conference on Neural Networks (IJCNN)
Year: 2024 | Conference Paper |
Improving model prediction capability based on continuously collected data is one of the biggest challenges in a mature, intelligent weather forecasting system. To tackle this problem, we propose an online incremental learning strategy for meteorological models with asynchronous updates. In short, we divide two different meteorological incremental learning settings according to the characteristics...Show More
Improving Conversational Recommendation System Through Personalized Preference Modeling and Knowledge Graph
Conversational recommendation systems (CRS) can actively discover users’ preferences and perform recommendations during conversations. The majority of works on CRS tend to focus on a single conversation and dig it using knowledge graphs, language models, etc. However, they often overlook the abundant and rich preference information that exists in the user's historical conversations. Meanwhile, end...Show More
Domain-Oriented Knowledge Transfer for Cross-Domain Recommendation
Guoshuai Zhao;Xiaolong Zhang;Hao Tang;Jialie Shen;Xueming Qian
Cross-Domain Recommendation (CDR) aims to alleviate the cold-start problem by transferring knowledge from a data-rich domain (source domain) to a data-sparse domain (target domain), where knowledge needs to be transferred through a bridge connecting the two domains. Therefore, constructing a bridge connecting the two domains is fundamental for enabling cross-domain recommendation. However, existin...Show More
Reason Generation for Point of Interest Recommendation Via a Hierarchical Attention-Based Transformer Model
Yuxia Wu;Guoshuai Zhao;Mingdi Li;Zhuocheng Zhang;Xueming Qian
Existing point-of-interest (POI) recommendation methods only show the direct recommendation results and lack the proper reasons for recommendation. In recent years, explainable recommendation has become an increasingly important subfield in recommendation systems. The aim of explainable recommendation is to provide a reason why an item is recommended to a user. In this way, it helps to improve the...Show More
Forecasting Treatment Outcomes Over Time Using Alternating Deep Sequential Models
Feng Wu;Guoshuai Zhao;Yuerong Zhou;Xueming Qian;Baedorf-Kassis Elias;Li-wei H Lehman
Medical decision making often relies on accurately forecasting future patient trajectories. Conventional approaches for patient progression modeling often do not explicitly model treatments when predicting patient trajectories and outcomes. In this paper, we propose Alternating Transformer (AL-Transformer) to jointly model treatment and clinical outcomes over time as alternating sequential models....Show More
Split-Check: Boosting Product Recognition via Instance-Level Retrieval
Chengxu Liu;Zongyang Da;Yuanzhi Liang;Yao Xue;Guoshuai Zhao;Xueming Qian
AI-based methods are shining across a variety of industries, especially unmanned retail. Product recognition is the problem of recognizing the category and quantity of products (e.g., beverages and mineral water) in intelligent unmanned vending machines (UVMs) to automatic checkout during purchase. However, for similar products in hundreds of categories, the existing method is not accurate enough....Show More
Sequential recommendation mines the user's interaction sequence or time information to get better recommendations and thus is gaining more and more attention. Existing sequential recommendations tend to build new models, and the study of the loss function is seriously neglected. Despite the increasing attention paid to contrastive learning recently, we believe that the key to contrastive learning ...Show More
Improved Continually Evolved Classifiers for Few-Shot Class-Incremental Learning
Few-shot class-incremental learning (FSCIL) aims to continually learn new classes using a few samples while not forgetting the old classes. The scarcity of new training data will seriously destroy the model’s stability and plasticity. Continually Evolved Classifiers (CEC) (Zhang et al., 2021), a kind of framework, maintains the stability by freezing the encoder and achieves the plasticity by evolv...Show More
Practical applications with visual question answering (VQA) systems are challenging, and recent research has aimed at investigating this important field. Many issues related to real-world VQA applications must be considered. Although existing methods have focused on adding external knowledge and other descriptive information to assist in reasoning, they are limited by the impact of information ret...Show More
Product Recognition for Unmanned Vending Machines
Chengxu Liu;Zongyang Da;Yuanzhi Liang;Yao Xue;Guoshuai Zhao;Xueming Qian
Recently, the emerging concept of “unmanned retail” has drawn more and more attention, and the unmanned retail based on the intelligent unmanned vending machines (UVMs) scene has great market demand. However, existing product recognition methods for intelligent UVMs cannot adapt to large-scale categories and have insufficient accuracy. In this article, we propose a method for large-scale categorie...Show More
Fine-Grained Conditional Convolution Network With Geographic Features for Temperature Prediction
Cheng Zhang;Guoshuai Zhao;Junjiao Liu;Xingjun Zhang;Xueming Qian
Short-to-medium term temperature prediction in high resolution is a very challenging task, involving meteorology, physics, mathematics, geography, and many other subjects. Its purpose is to fit a complex function from historical meteorological data to predict the future 1–5 days temperature, which is a typical spatio-temporal prediction problem. Meteorological data show complex correlations in loc...Show More
In image-text matching fields, one of the keys to improving performance is to extract features with more semantic information. Existing works demonstrate that semantic enrichment through knowledge expansion can improve performance. Most of them expand image features, however, the shortage of semantic information in text modality and the unilateral character of the view are often bottlenecks that l...Show More
State Graph Reasoning for Multimodal Conversational Recommendation
Yuxia Wu;Lizi Liao;Gangyi Zhang;Wenqiang Lei;Guoshuai Zhao;Xueming Qian;Tat-Seng Chua
Conversational recommendation system (CRS) attracts increasing attention in various application domains such as retail and travel. It offers an effective way to capture users’ dynamic preferences with multi-turn conversations. However, most current studies center on the recommendation aspect while over-simplifying the conversation process. The negligence of complexity in data structure and convers...Show More
What You Like, What I Am: Online Dating Recommendation via Matching Individual Preferences With Features
Dating recommendation becomes a critical task since the rapidly development of the online dating sites and it is beneficial for users to find their ideal relationships from a large number of registered members. Different users usually have different tastes when choosing their dating partners. Therefore, it is necessary to distinguish the user’s personal features and preferences in dating recommend...Show More
Joint Reason Generation and Rating Prediction for Explainable Recommendation
Most recommendation systems focus on predicting rating or finding aspect information in reviews to understand user preferences and item properties. However, these methods ignore the effectiveness and persuasiveness of recommendation results. Consequently, explainable recommendation, namely providing recommendation results with recommendation reasons at the same time, has attracted increasing atten...Show More
Top-k recommendation is a fundamental task in recommendation systems that is generally learned by comparing positive and negative pairs. The contrastive loss (CL) is the key in contrastive learning that has recently received more attention, and we find that it is well suited for top-k recommendations. However, CL is problematic because it treats the importance of the positive and negative samples ...Show More
Sequential recommendations aim to predict the user’s next behaviors items based on their successive historical behaviors sequence. It has been widely applied in lots of online services. However, current sequential recommendations use the adjacent behaviors to capture the features of the sequence, ignoring the features among nonadjacent sequential items and the summarized features of the sequence. ...Show More
Personalized Long- and Short-term Preference Learning for Next POI Recommendation
Next POI recommendation has been studied extensively in recent years. The goal is to recommend next POI for users at specific time given users’ historical check-in data. Therefore, it is crucial to model both users’ general taste and recent sequential behaviors. Moreover, different users show different dependencies on the two parts. However, most existing methods learn the same dependencies for di...Show More
Deep Multi-View Subspace Clustering With Unified and Discriminative Learning
Qianqian Wang;Jiafeng Cheng;Quanxue Gao;Guoshuai Zhao;Licheng Jiao
Deep multi-view subspace clustering has achieved promising performance compared with other multi-view clustering. However, existing deep multi-view subspace clustering only considers the global structure for all views, and they ignore the local geometric structure among each view. In addition, they cannot learn discriminative feature on different clusters of different views, i.e., inter-cluster di...Show More
Continual Multiview Task Learning via Deep Matrix Factorization
The state-of-the-art multitask multiview (MTMV) learning tackles a scenario where multiple tasks are related to each other via multiple shared feature views. However, in many real-world scenarios where a sequence of the multiview task comes, the higher storage requirement and computational cost of retraining previous tasks with MTMV models have presented a formidable challenge for this lifelong le...Show More
CAPER: Context-Aware Personalized Emoji Recommendation
With the popularity of social platforms, emoji appears and becomes extremely popular with a large number of users. It expresses more beyond plaintexts and makes the content more vivid. Using appropriate emojis in messages and microblog posts makes you lovely and friendly. Recently, emoji recommendation becomes a significant task since it is hard to choose the appropriate one from thousands of emoj...Show More
Location Recommendation for Enterprises by Multi-Source Urban Big Data Analysis
Guoshuai Zhao;Tianlei Liu;Xueming Qian;Tao Hou;Huan Wang;Xingsong Hou;Zhetao Li
Effective location recommendation is an important problem in both research and industry. Much research has focused on personalized recommendation for users. However, there are more uses such as site selection for firms and factories. In this study, we try to solve site selection problem by recommending some locations satisfying special requirements. There are many factors affecting it, including f...Show More
Recently, more and more people have the preference for obtaining the latest news and posting their views relying on social media. In this way, some opinion leaders would ultimately get a large number of followers. Because of the significant influence imposed by their social accounts, some of them start to post native advertisements in their articles, and the articles that fall within the scope of ...Show More
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