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Articles 1 - 7 of 7
Full-Text Articles in Physical Sciences and Mathematics
Improving E-Commerce Product Recommendation Using Semantic Context And Sequential Historical Purchases, Mahreen Nasir, C. I. Ezeife, Abdulrauf Gidado
Improving E-Commerce Product Recommendation Using Semantic Context And Sequential Historical Purchases, Mahreen Nasir, C. I. Ezeife, Abdulrauf Gidado
Computer Science Publications
Collaborative Filtering (CF)-based recommendation methods suffer from (i) sparsity (have low user–item interactions) and (ii) cold start (an item cannot be recommended if no ratings exist). Systems using clustering and pattern mining (frequent and sequential) with similarity measures between clicks and purchases for next-item recommendation cannot perform well when the matrix is sparse, due to rapid increase in number of items. Additionally, they suffer from: (i) lack of personalization: patterns are not targeted for a specific customer and (ii) lack of semantics among recommended items: they can only recommend items that exist as a result of a matching rule generated …
Multi-Modal Transformers Excel At Class-Agnostic Object Detection, Muhammad Maaz, Hanoona Bangalath Rasheed, Salman Hameed Khan, Fahad Shahbaz Khan, Rao Muhammad Anwer, Ming-Hsuan Yang
Multi-Modal Transformers Excel At Class-Agnostic Object Detection, Muhammad Maaz, Hanoona Bangalath Rasheed, Salman Hameed Khan, Fahad Shahbaz Khan, Rao Muhammad Anwer, Ming-Hsuan Yang
Computer Vision Faculty Publications
What constitutes an object? This has been a longstanding question in computer vision. Towards this goal, numerous learning-free and learning-based approaches have been developed to score objectness. However, they generally do not scale well across new domains and for unseen objects. In this paper, we advocate that existing methods lack a top-down supervision signal governed by human-understandable semantics. To bridge this gap, we explore recent Multi-modal Vision Transformers (MViT) that have been trained with aligned image-text pairs. Our extensive experiments across various domains and novel objects show the state-of-the-art performance of MViTs to localize generic objects in images. Based on …
Cosy: Counterfactual Syntax For Cross-Lingual Understanding, Sicheng Yu, Hao Zhang, Yulei Niu, Qianru Sun, Jing Jiang
Cosy: Counterfactual Syntax For Cross-Lingual Understanding, Sicheng Yu, Hao Zhang, Yulei Niu, Qianru Sun, Jing Jiang
Research Collection School Of Computing and Information Systems
Pre-trained multilingual language models, e.g., multilingual-BERT, are widely used in cross-lingual tasks, yielding the state-of-the-art performance. However, such models suffer from a large performance gap between source and target languages, especially in the zero-shot setting, where the models are fine-tuned only on English but tested on other languages for the same task. We tackle this issue by incorporating language-agnostic information, specifically, universal syntax such as dependency relations and POS tags, into language models, based on the observation that universal syntax is transferable across different languages. Our approach, named COunterfactual SYntax (COSY), includes the design of SYntax-aware networks as well as …
Ontologies For Geospatial Information: Progress And Challenges Ahead, Christophe Claramunt
Ontologies For Geospatial Information: Progress And Challenges Ahead, Christophe Claramunt
Journal of Spatial Information Science
Over the past 50 years or so the representation of spatial information within computerized systems has been widely addressed and developed in order to provide suitable data manipulation, analysis, and visualisation mechanisms. The range of applications is unlimited and nowadays impacts almost all sciences and practices. However, current conceptualisations and numerical representations of geospatial information still require the development of richer abstract models that match the complexity of spatial and temporal information. Geospatial ontologies are promising modelling alternatives that might favour the implementation and sharing of geographical information. The objective of this vision paper is to provide a short introduction …
Data-Driven Agriculture For Rural Smallholdings, Kerry Taylor, Martin Amidy
Data-Driven Agriculture For Rural Smallholdings, Kerry Taylor, Martin Amidy
Journal of Spatial Information Science
Spatial information science has a critical role to play in meeting the major challenges facing society in the coming decades, including feeding a population of 10 billion by 2050, addressing environmental degradation, and acting on climate change. Agriculture and agri-food value-chains, dependent on spatial information, are also central. Due to agriculture's dual role as not only a producer of food, fibre and fuel, but also as a major land, water and energy consumer, agriculture is at the centre of both the food-water-energy-environment nexus and resource security debates. The recent confluence of a number of advances in data analytics, cloud computing, …
Dehumor: Visual Analytics For Decomposing Humor, Xingbo Wang, Yao Ming, Tongshuang Wu, Haipeng Zeng, Yong Wang, Huamin Qu
Dehumor: Visual Analytics For Decomposing Humor, Xingbo Wang, Yao Ming, Tongshuang Wu, Haipeng Zeng, Yong Wang, Huamin Qu
Research Collection School Of Computing and Information Systems
Despite being a critical communication skill, grasping humor is challenginga successful use of humor requires a mixture of both engaging content build-up and an appropriate vocal delivery (e.g., pause). Prior studies on computational humor emphasize the textual and audio features immediately next to the punchline, yet overlooking longer-term context setup. Moreover, the theories are usually too abstract for understanding each concrete humor snippet. To fill in the gap, we develop DeHumor, a visual analytical system for analyzing humorous behaviors in public speaking. To intuitively reveal the building blocks of each concrete example, DeHumor decomposes each humorous video into multimodal features …
Cross-Modal Food Retrieval: Learning A Joint Embedding Of Food Images And Recipes With Semantic Consistency And Attention Mechanism;, Hao Wang, Doyen Sahoo, Chenghao Liu, Ke Shu, Achananuparp Palakorn, Ee Peng Lim, Steven Hoi
Cross-Modal Food Retrieval: Learning A Joint Embedding Of Food Images And Recipes With Semantic Consistency And Attention Mechanism;, Hao Wang, Doyen Sahoo, Chenghao Liu, Ke Shu, Achananuparp Palakorn, Ee Peng Lim, Steven Hoi
Research Collection School Of Computing and Information Systems
Food retrieval is an important task to perform analysis of food-related information, where we are interested in retrieving relevant information about the queried food item such as ingredients, cooking instructions, etc. In this paper, we investigate cross-modal retrieval between food images and cooking recipes. The goal is to learn an embedding of images and recipes in a common feature space, such that the corresponding image-recipe embeddings lie close to one another. Two major challenges in addressing this problem are 1) large intra-variance and small inter-variance across cross-modal food data; and 2) difficulties in obtaining discriminative recipe representations. To address these …