1,104 research outputs found

    Video Stream Retrieval of Unseen Queries using Semantic Memory

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    Retrieval of live, user-broadcast video streams is an under-addressed and increasingly relevant challenge. The on-line nature of the problem requires temporal evaluation and the unforeseeable scope of potential queries motivates an approach which can accommodate arbitrary search queries. To account for the breadth of possible queries, we adopt a no-example approach to query retrieval, which uses a query's semantic relatedness to pre-trained concept classifiers. To adapt to shifting video content, we propose memory pooling and memory welling methods that favor recent information over long past content. We identify two stream retrieval tasks, instantaneous retrieval at any particular time and continuous retrieval over a prolonged duration, and propose means for evaluating them. Three large scale video datasets are adapted to the challenge of stream retrieval. We report results for our search methods on the new stream retrieval tasks, as well as demonstrate their efficacy in a traditional, non-streaming video task.Comment: Presented at BMVC 2016, British Machine Vision Conference, 201

    Objects2action: Classifying and localizing actions without any video example

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    The goal of this paper is to recognize actions in video without the need for examples. Different from traditional zero-shot approaches we do not demand the design and specification of attribute classifiers and class-to-attribute mappings to allow for transfer from seen classes to unseen classes. Our key contribution is objects2action, a semantic word embedding that is spanned by a skip-gram model of thousands of object categories. Action labels are assigned to an object encoding of unseen video based on a convex combination of action and object affinities. Our semantic embedding has three main characteristics to accommodate for the specifics of actions. First, we propose a mechanism to exploit multiple-word descriptions of actions and objects. Second, we incorporate the automated selection of the most responsive objects per action. And finally, we demonstrate how to extend our zero-shot approach to the spatio-temporal localization of actions in video. Experiments on four action datasets demonstrate the potential of our approach

    Active Transfer Learning with Zero-Shot Priors: Reusing Past Datasets for Future Tasks

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    How can we reuse existing knowledge, in the form of available datasets, when solving a new and apparently unrelated target task from a set of unlabeled data? In this work we make a first contribution to answer this question in the context of image classification. We frame this quest as an active learning problem and use zero-shot classifiers to guide the learning process by linking the new task to the existing classifiers. By revisiting the dual formulation of adaptive SVM, we reveal two basic conditions to choose greedily only the most relevant samples to be annotated. On this basis we propose an effective active learning algorithm which learns the best possible target classification model with minimum human labeling effort. Extensive experiments on two challenging datasets show the value of our approach compared to the state-of-the-art active learning methodologies, as well as its potential to reuse past datasets with minimal effort for future tasks

    Effect of Dietary cis and trans fatty acids on serum lipoprotein[a] levels in humans

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    Serum lipoprotein[a] (Lp[a]) is a strong risk factor for coronary heart disease. We therefore examined the effect of dietary fatty acid composition on serum Lp[a] levels in three strictly controlled experiments with healthy normocholesterolemic men and women. In Expt. I, 58 subjects consumed a control diet high in saturated fatty acids for 17 days. For the next 36 days, 6.5% of total energy intake from saturated fatty acids was replaced by monounsaturates plus polyunsaturates (monounsaturated fatty acid diet; n = 29) or by polyunsaturates alone (polyunsaturated fatty acid diet; n = 29). Both diets caused a slight, nonsignificant, increase in median Lp[a] levels, with no difference between diets. In Expt. II, 10% of energy from the cholesterol-raising saturated fatty acids (lauric, myristic, and palmitic acid) was replaced by oleic acid or by trans-monounsaturated fatty acids. Each of the 59 participants received each diet for 3 weeks in random order. The median level of Lp[a] was 26 mg/l on the saturated fatty acid diet; it increased to 32 mg/l (P less than 0.020) on the oleic acid diet and to 45 mg/l (P less than 0.001) on the trans-fatty acid diet. The difference in Lp[a] between the trans-fatty acid and the oleic acid diets was also highly significant (P less than 0.001). Expt. III involved 56 subjects; all received 8% of energy from stearic acid, from linoleic acid, or from trans-monounsaturates, for 3 weeks each. All other nutrients were equal.

    Bilateral sagittal split osteotomy by the splitter-separator technique: technical aspects, safety, and predictability

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    The goal of this thesis is to prove the safety and predictability of BSSO by the splitter__ separator technique in an extensive study of its possible major sequelae. The revised BSSO technique will be assessed by the following means: 1. Reviewing both BSSO techniques and their incidences of postoperative NSD of the IAN (chapter 2) 2. Analyzing fracture patterns in cadaveric mandibles (chapters 5 and 6) 3. Measuring postoperative hypoesthesia of the IAN in a prospective study (chapter 3) 4. Examining stability during adolescence (chapter 4) 5. Examining bad splits in a retrospective study (chapter 7) 6. Reviewing specific applications (chapters 8 and 9).UBL - phd migration 201
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