259 research outputs found

    Quantifying Facial Age by Posterior of Age Comparisons

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    We introduce a novel approach for annotating large quantity of in-the-wild facial images with high-quality posterior age distribution as labels. Each posterior provides a probability distribution of estimated ages for a face. Our approach is motivated by observations that it is easier to distinguish who is the older of two people than to determine the person's actual age. Given a reference database with samples of known ages and a dataset to label, we can transfer reliable annotations from the former to the latter via human-in-the-loop comparisons. We show an effective way to transform such comparisons to posterior via fully-connected and SoftMax layers, so as to permit end-to-end training in a deep network. Thanks to the efficient and effective annotation approach, we collect a new large-scale facial age dataset, dubbed `MegaAge', which consists of 41,941 images. Data can be downloaded from our project page mmlab.ie.cuhk.edu.hk/projects/MegaAge and github.com/zyx2012/Age_estimation_BMVC2017. With the dataset, we train a network that jointly performs ordinal hyperplane classification and posterior distribution learning. Our approach achieves state-of-the-art results on popular benchmarks such as MORPH2, Adience, and the newly proposed MegaAge.Comment: To appear on BMVC 2017 (oral) revised versio

    Unified Pretraining Target Based Video-music Retrieval With Music Rhythm And Video Optical Flow Information

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    Background music (BGM) can enhance the video's emotion. However, selecting an appropriate BGM often requires domain knowledge. This has led to the development of video-music retrieval techniques. Most existing approaches utilize pretrained video/music feature extractors trained with different target sets to obtain average video/music-level embeddings. The drawbacks are two-fold. One is that different target sets for video/music pretraining may cause the generated embeddings difficult to match. The second is that the underlying temporal correlation between video and music is ignored. In this paper, our proposed approach leverages a unified target set to perform video/music pretraining and produces clip-level embeddings to preserve temporal information. The downstream cross-modal matching is based on the clip-level features with embedded music rhythm and optical flow information. Experiments demonstrate that our proposed method can achieve superior performance over the state-of-the-art methods by a significant margin

    Transformation of \u3ci\u3eFusarium verticillioides\u3c/i\u3e with a polyketide gene cluster isolated from a fungal endophyte activates the biosynthesis of fusaric acid

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    A large number of bioactive natural products have been isolated from plant endophytic fungi. However, molecular mechanisms for the biosynthesis of these metabolites have lagged behind because genetic and biochemical studies are difficult to perform within many of the endophytes. In this work, we describe our attempt to express a putative mycoepoxydiene (MED) biosynthetic gene cluster in Fusarium verticillioides, which has a well-developed genetic system for the study fungal polyketide biosynthesis. MED was isolated from Phomopsis sp. A123, a fungal endophyte of the mangrove plant, Kandelia candel. It has several unusual structural features and interesting biological activities. Integration of this Phomopsis gene cluster into the F. verticillioides genome led to the biosynthesis of multiple metabolites. The most highly activated metabolite was isolated and its structure was shown by 1D- and 2D-NMR to be fusaric acid, which is a mycotoxin in Fusarium species and is implicated in fungal pathogenesis. Although fusaric acid was isolated more than 70 years ago, its biosynthetic mechanism remains unclear. These transformants produced 30–35 mg fusaric acid per 100 ml culture. The high level production of fusaric acid will greatly facilitate the genetic and biochemical study of its biosynthetic mechanism. Although we have not detected MED or its analogs from the heterologous host, this work represents the first attempt to express a fungal endophytic gene cluster in a Fusarium species

    “Best Employers”: The Impacts of Employee Reviews and Employer Awards on Job Seekers’ Application Intentions

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    While hospitality researchers have examined the impacts of user-generated content on customers, research regarding the impacts of employee reviews on job seekers’ application intentions is scarce. Yet, labor shortages in the hospitality industry have been amplified in recent years. The tight job market requires organizations to use aggressive and proactive recruitment strategies. As online employee reviews can attract both active and passive job seekers, organizations are increasingly advertising their jobs on these sites. This study draws on the elaboration likelihood model (ELM) and tests the boundary condition of work experience on the effects of overall star-ratings and employer awards on job seekers’ application intention. Through an experimental survey, this study sought to fill the gap regarding the impacts of employee-generated star-ratings and employer awards on job seekers’ application intentions. Both star-ratings and employer awards are positively related to organizational prestige. Hospitality work experience moderates the relationship between star-ratings and organizational prestige. The relationship is stronger for novice job seekers than for experienced job seekers. Organizational prestige, in turn, increases job seekers’ application intentions. Our findings extend the recruitment literature and highlight the potential usage of ELM as an explorative framework in hospitality recruitment research. The study also provides suggestions for hospitality employers to attract job seekers

    UAV photogrammetry in intertidal mudflats: accuracy, efficiency, and potential for integration with satellite imagery

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    The rapid, up-to-date, cost-effective acquisition and tracking of intertidal topography are the fundamental basis for timely, high-priority protection and restoration of the intertidal zone. The low cost, ease of use, and flexible UAV-based photogrammetry have revolutionized the monitoring of intertidal zones. However, the capability of the RTK-assisted UAV photogrammetry without ground control points, the impact of flight configuration difference, the presence of surface water in low-lying intertidal areas on the photogrammetric accuracy, and the potential of UAV/satellite Synergy remain unknown. In this paper, we used an RTK-assisted UAV to assess the impact of the above-mentioned considerations quantitatively on photogrammetric results in the context of annual monitoring of the Chongming Dongtan Nature Reserve, China based on an optimal flight combination. The results suggested that (1) RTK-assisted UAVs can obtain high-accuracy topographic data with a vertical RMSE of 3.1 cm, without the need for ground control points. (2) The effect of flight altitude on topographic accuracy was most significant and also nonlinear. (3) The elevation obtained by UAV photogrammetry was overestimated by approximately 2.4 cm in the low-lying water-bearing regions. (4) The integration of UAV and satellite observations can increase the accuracy of satellite-based waterline methods by 51%. These quantitative results not only provide scientific insights and guidelines for the balance between accuracy and efficiency in utilizing UAV-based intertidal monitoring, but also demonstrate the great potential of combined UAV and satellite observations in identifying coastal erosion hotspots. This establishes high-priority protection mechanisms and promotes coastal restoration
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