654 research outputs found

    Adaptive Sampling for Low Latency Vision Processing

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    A qualitative exploratory study: Using medical students’ experiences to review the role of a rural clinical attachment in KwaZulu-Natal

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    Objectives: There are challenges when it is considered that a main role of a rural clinical attachment for medical students is to encourage students to return after graduation to practise in rural areas. This view may lead to the relative neglect of other potential valuable roles with regard to rural  exposure. This paper draws on the Force Field Model of teacher development to describe medical students’ experiences, illustrate the complexity of interacting factors during rural exposure, caution that experiences cannot be predicted and highlight the positive incentives of a rural clinical attachment.Design: The design was explorative, descriptive and qualitative.Setting: The study setting was a district hospital in rural KwaZulu-Natal.Subjects: The participants were four final-year medical students who had completed a compulsory attachment during their Family Medicine rotation.Outcome measures: Data were collected using photo elicitation and analysed using the Force Field Model.Results: The participants felt that overall the experience was positive. The effect of biography and contextual forces were not as strong as expected. Institutional forces were important and programmatic forces tended to have a negative effect on experiences. The participants particularly enjoyed being acknowledged and felt empathy for the difficult tasks of doctors.Conclusion: The potential role of a clinical attachment may go beyond attracting students to practise in rural areas. The experience can be beneficial, irrespective of where the student decides to practise after graduation. There is a need for a review of the rural attachment curriculum and paedagogy. Caution should be used when screening medical students for suitability to work in rural areas prior to rural exposure.Keywords: rural clinical attachment, family medicine rotation, medical students, experiences, Force Field Mode

    Hierarchical Subquery Evaluation for Active Learning on a Graph

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    To train good supervised and semi-supervised object classifiers, it is critical that we not waste the time of the human experts who are providing the training labels. Existing active learning strategies can have uneven performance, being efficient on some datasets but wasteful on others, or inconsistent just between runs on the same dataset. We propose perplexity based graph construction and a new hierarchical subquery evaluation algorithm to combat this variability, and to release the potential of Expected Error Reduction. Under some specific circumstances, Expected Error Reduction has been one of the strongest-performing informativeness criteria for active learning. Until now, it has also been prohibitively costly to compute for sizeable datasets. We demonstrate our highly practical algorithm, comparing it to other active learning measures on classification datasets that vary in sparsity, dimensionality, and size. Our algorithm is consistent over multiple runs and achieves high accuracy, while querying the human expert for labels at a frequency that matches their desired time budget.Comment: CVPR 201

    MegaParallax: Casual 360° Panoramas with Motion Parallax

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    The ubiquity of smart mobile devices, such as phones and tablets, enables users to casually capture 360° panoramas with a single camera sweep to share and relive experiences. However, panoramas lack motion parallax as they do not provide different views for different viewpoints. The motion parallax induced by translational head motion is a crucial depth cue in daily life. Alternatives, such as omnidirectional stereo panoramas, provide different views for each eye (binocular disparity), but they also lack motion parallax as the left and right eye panoramas are stitched statically. Methods based on explicit scene geometry reconstruct textured 3D geometry, which provides motion parallax, but suffers from visible reconstruction artefacts. The core of our method is a novel multi-perspective panorama representation, which can be casually captured and rendered with motion parallax for each eye on the fly. This provides a more realistic perception of panoramic environments which is particularly useful for virtual reality applications. Our approach uses a single consumer video camera to acquire 200–400 views of a real 360° environment with a single sweep. By using novel-view synthesis with flow-based blending, we show how to turn these input views into an enriched 360° panoramic experience that can be explored in real time, without relying on potentially unreliable reconstruction of scene geometry. We compare our results with existing omnidirectional stereo and image-based rendering methods to demonstrate the benefit of our approach, which is the first to enable casual consumers to capture and view high-quality 360° panoramas with motion parallax.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 66599

    Likelihood-based Out-of-Distribution Detection with Denoising Diffusion Probabilistic Models

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    Out-of-Distribution detection between dataset pairs has been extensively explored with generative models. We show that likelihood-based Out-of-Distribution detection can be extended to diffusion models by leveraging the fact that they, like other likelihood-based generative models, are dramatically affected by the input sample complexity. Currently, all Out-of-Distribution detection methods with Diffusion Models are reconstruction-based. We propose a new likelihood ratio for Out-of-Distribution detection with Deep Denoising Diffusion Models, which we call the Complexity Corrected Likelihood Ratio. Our likelihood ratio is constructed using Evidence Lower-Bound evaluations from an individual model at various noising levels. We present results that are comparable to state-of-the-art Out-of-Distribution detection methods with generative models.Comment: 9 pages (main paper), 3 pages (acknowledgements & references), 3 figures, 2 tables, 1 algorithm, work accepted for BMVC 202
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