Video Stream Retrieval of Unseen Queries using Semantic Memory

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

Type 2 diabetes mellitus and skeletal muscle metabolic function.

AB - Type 2 diabetic patients are characterized by a decreased fat oxidative capacity and high levels of circulating free fatty acids (FFAs). The latter is known to cause insulin resistance, in particularly in skeletal muscle, by reducing insulin stimulated glucose uptake, most likely via accumulation of lipid inside the muscle cell. A reduced skeletal muscle oxidative capacity can exaggerate this. Furthermore, type 2 diabetes is associated with impaired metabolic flexibility, i.e. an impaired switching from fatty acid to glucose oxidation in response to insulin. Thus, a reduced fat oxidative capacity and metabolic inflexibility are important components of skeletal muscle insulin resistance. The cause of these derangements in skeletal muscle of type 2 diabetic patients remains to be elucidated. An impaired mitochondrial function is a likely candidate. Evidence from both in vivo and ex vivo studies supports the idea that an impaired skeletal muscle mitochondrial function is related to the development of insulin resistance and type 2 diabetes mellitus. A decreased mitochondrial oxidative capacity in skeletal muscle was revealed in diabetic patients, using in vivo 31-Phosphorus Magnetic Resonance Spectroscopy (31P-MRS). However, quantification of mitochondrial function using ex vivo high-resolution respirometry revealed opposite results. Future (human) studies should challenge this concept of impaired mitochondrial function underlying metabolic defects and prove if mitochondria are truly functional impaired in insulin resistance, or low in number, and whether it represents the primary starting point of pathogenesis of insulin resistance, or is just an other feature of the insulin resistant stat

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

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

Objects2action: Classifying and localizing actions without any video example

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

Influence of oxygen pressure on the fs laserinduced oxidation of molybdenum thin films

We present a study of femtosecond (1028 nm, 230 fs, 54.7 MHz) laser processing on molybdenum (Mo) thin films. Irradiations were done under ambient air as well as pure oxygen (O2) at various gauge pressures (4, 8, 12 and 16 psi). Our results indicate that the high heating rates associated with laser processing allow the production of different molybdenum oxides. Raman spectroscopy and scanning electron microscopy are used to characterize the molybdenum oxidation for the different irradiation and oxygen pressures parameters chosen showing a high correlation between well-defined oxidation zones and the oxygen pressure surrounding the samples during the irradiation of the Mo thin films

Adding New Tasks to a Single Network with Weight Transformations using Binary Masks

Visual recognition algorithms are required today to exhibit adaptive abilities. Given a deep model trained on a specific, given task, it would be highly desirable to be able to adapt incrementally to new tasks, preserving scalability as the number of new tasks increases, while at the same time avoiding catastrophic forgetting issues. Recent work has shown that masking the internal weights of a given original conv-net through learned binary variables is a promising strategy. We build upon this intuition and take into account more elaborated affine transformations of the convolutional weights that include learned binary masks. We show that with our generalization it is possible to achieve significantly higher levels of adaptation to new tasks, enabling the approach to compete with fine tuning strategies by requiring slightly more than 1 bit per network parameter per additional task. Experiments on two popular benchmarks showcase the power of our approach, that achieves the new state of the art on the Visual Decathlon Challenge