Models, code, and papers for "Wen-Hua Chen":

Agricultural Robotics: The Future of Robotic Agriculture

Aug 02, 2018
Tom Duckett, Simon Pearson, Simon Blackmore, Bruce Grieve, Wen-Hua Chen, Grzegorz Cielniak, Jason Cleaversmith, Jian Dai, Steve Davis, Charles Fox, Pål From, Ioannis Georgilas, Richie Gill, Iain Gould, Marc Hanheide, Alan Hunter, Fumiya Iida, Lyudmila Mihalyova, Samia Nefti-Meziani, Gerhard Neumann, Paolo Paoletti, Tony Pridmore, Dave Ross, Melvyn Smith, Martin Stoelen, Mark Swainson, Sam Wane, Peter Wilson, Isobel Wright, Guang-Zhong Yang

Agri-Food is the largest manufacturing sector in the UK. It supports a food chain that generates over {\pounds}108bn p.a., with 3.9m employees in a truly international industry and exports {\pounds}20bn of UK manufactured goods. However, the global food chain is under pressure from population growth, climate change, political pressures affecting migration, population drift from rural to urban regions and the demographics of an aging global population. These challenges are recognised in the UK Industrial Strategy white paper and backed by significant investment via a Wave 2 Industrial Challenge Fund Investment ("Transforming Food Production: from Farm to Fork"). Robotics and Autonomous Systems (RAS) and associated digital technologies are now seen as enablers of this critical food chain transformation. To meet these challenges, this white paper reviews the state of the art in the application of RAS in Agri-Food production and explores research and innovation needs to ensure these technologies reach their full potential and deliver the necessary impacts in the Agri-Food sector.

* UK-RAS Network White Papers, ISSN 2398-4414 

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Joint Enhancement and Denoising Method via Sequential Decomposition

Apr 28, 2018
Xutong Ren, Mading Li, Wen-Huang Cheng, Jiaying Liu

Many low-light enhancement methods ignore intensive noise in original images. As a result, they often simultaneously enhance the noise as well. Furthermore, extra denoising procedures adopted by most methods ruin the details. In this paper, we introduce a joint low-light enhancement and denoising strategy, aimed at obtaining well-enhanced low-light images while getting rid of the inherent noise issue simultaneously. The proposed method performs Retinex model based decomposition in a successive sequence, which sequentially estimates a piece-wise smoothed illumination and a noise-suppressed reflectance. After getting the illumination and reflectance map, we adjust the illumination layer and generate our enhancement result. In this noise-suppressed sequential decomposition process we enforce the spatial smoothness on each component and skillfully make use of weight matrices to suppress the noise and improve the contrast. Results of extensive experiments demonstrate the effectiveness and practicability of our method. It performs well for a wide variety of images, and achieves better or comparable quality compared with the state-of-the-art methods.

* Accepted by ISCAS 2018 

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Time Matters: Multi-scale Temporalization of Social Media Popularity

Dec 12, 2017
Bo Wu, Wen-Huang Cheng, Yongdong Zhang, Tao Mei

The evolution of social media popularity exhibits rich temporality, i.e., popularities change over time at various levels of temporal granularity. This is influenced by temporal variations of public attentions or user activities. For example, popularity patterns of street snap on Flickr are observed to depict distinctive fashion styles at specific time scales, such as season-based periodic fluctuations for Trench Coat or one-off peak in days for Evening Dress. However, this fact is often overlooked by existing research of popularity modeling. We present the first study to incorporate multiple time-scale dynamics into predicting online popularity. We propose a novel computational framework in the paper, named Multi-scale Temporalization, for estimating popularity based on multi-scale decomposition and structural reconstruction in a tensor space of user, post, and time by joint low-rank constraints. By considering the noise caused by context inconsistency, we design a data rearrangement step based on context aggregation as preprocessing to enhance contextual relevance of neighboring data in the tensor space. As a result, our approach can leverage multiple levels of temporal characteristics and reduce the noise of data decomposition to improve modeling effectiveness. We evaluate our approach on two large-scale Flickr image datasets with over 1.8 million photos in total, for the task of popularity prediction. The results show that our approach significantly outperforms state-of-the-art popularity prediction techniques, with a relative improvement of 10.9%-47.5% in terms of prediction accuracy.

* accepted in ACM Multimedia 2016 

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SMP Challenge: An Overview of Social Media Prediction Challenge 2019

Oct 04, 2019
Bo Wu, Wen-Huang Cheng, Peiye Liu, Zhaoyang Zeng, Jiebo Luo

"SMP Challenge" aims to discover novel prediction tasks for numerous data on social multimedia and seek excellent research teams. Making predictions via social multimedia data (e.g. photos, videos or news) is not only helps us to make better strategic decisions for the future, but also explores advanced predictive learning and analytic methods on various problems and scenarios, such as multimedia recommendation, advertising system, fashion analysis etc. In the SMP Challenge at ACM Multimedia 2019, we introduce a novel prediction task Temporal Popularity Prediction, which focuses on predicting future interaction or attractiveness (in terms of clicks, views or likes etc.) of new online posts in social media feeds before uploading. We also collected and released a large-scale SMPD benchmark with over 480K posts from 69K users. In this paper, we define the challenge problem, give an overview of the dataset, present statistics of rich information for data and annotation and design the accuracy and correlation evaluation metrics for temporal popularity prediction to the challenge.

* ACM MM 2019 

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Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks

Dec 12, 2017
Bo Wu, Wen-Huang Cheng, Yongdong Zhang, Qiushi Huang, Jintao Li, Tao Mei

Prediction of popularity has profound impact for social media, since it offers opportunities to reveal individual preference and public attention from evolutionary social systems. Previous research, although achieves promising results, neglects one distinctive characteristic of social data, i.e., sequentiality. For example, the popularity of online content is generated over time with sequential post streams of social media. To investigate the sequential prediction of popularity, we propose a novel prediction framework called Deep Temporal Context Networks (DTCN) by incorporating both temporal context and temporal attention into account. Our DTCN contains three main components, from embedding, learning to predicting. With a joint embedding network, we obtain a unified deep representation of multi-modal user-post data in a common embedding space. Then, based on the embedded data sequence over time, temporal context learning attempts to recurrently learn two adaptive temporal contexts for sequential popularity. Finally, a novel temporal attention is designed to predict new popularity (the popularity of a new user-post pair) with temporal coherence across multiple time-scales. Experiments on our released image dataset with about 600K Flickr photos demonstrate that DTCN outperforms state-of-the-art deep prediction algorithms, with an average of 21.51% relative performance improvement in the popularity prediction (Spearman Ranking Correlation).

* accepted in IJCAI-17 

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