Several stochastic models provide an effective framework to identify the temporal structure of audiovisual data. Most of them need as input a first video structure, i.e. connections between features and video events. Provided that this structure is given as input, the parameters are then estimated from training data. Bayesian networks offer an additional feature, namely structure learning, which allows the automatic construction of the model structure from training data. Structure learning obviously leads to an increased generality of the model building process. This paper investigates the trade-off between the increase of generality and the quality of the results in video analysis. We model video data using dynamic Bayesian networks (DBNs) where the static part of the network accounts for the correlations between low-level features extracted from the raw data and between these features and the events considered. It is precisely this part of the network whose structure is automatically constructed from training data. Experimental results on a commercial detection case study application show that, even though the model structure is determined in a non supervised manner, the resulting model is effective for the detection of commercial segments in video data.
We investigate the use of structure learning in Bayesian networks for a complex multimodal task of action detection in soccer videos. We illustrate that classical score-oriented structure learning algorithms, such as the K2 one whose usefulness has been demonstrated on simple tasks, fail in providing a good network structure for classification tasks where many correlated observed variables are necessary to make a decision. We then compare several structure learning objective functions, which aim at finding out the structure that yields the best classification results, extending existing solutions in the literature. Experimental results on a comprehensive data set of 7 videos show that a discriminative objective function based on conditional likelihood yields the best results, while augmented approaches offer a good compromise between learning speed and classification accuracy.
The professional content production workflow is no longer concentrated on the only broadcast distribution channel. It has to address other distribution means, such as mobile devices, Internet or IPTV. The associated multi-distribution systems are inserted right after the production studio to help automating the process of content adaptation. The resulting workflow is shown in Figure 1. To ensure the metadata permanence and consistency, an ideal production workflow should be able to propagate these metadata in the whole production chain. The metadata produced in the workflow should be re-usable anywhere. This is actually not the case, as some key elements of production studios (e.g. the switcher) do not propagate such information, and most of the time the metadata produced are lost and should be generated again.
Context. The exascale super-computers becoming available rely on hybrid energy-efficient architectures that involve an accelerator such as a graphics processing unit (GPU). Leveraging the computational power of these machines often means a significant rewrite of the numerical tools each time a new architecture becomes available. Aims. We present IDEFIX, a new code for astrophysical flows that relies on the KOKKOS meta-programming library to guarantee performance portability on a wide variety of architectures while keeping the code as simple as possible to the user. Methods. IDEFIX is based on a Godunov finite-volume method that solves the nonrelativistic hydrodynamical (HD) and magnetohy-drodynamical (MHD) equations on various grid geometries. IDEFIX includes a large choice of solvers and several additional modules (constrained transport, orbital advection, nonideal MHD), allowing users to address complex astrophysical problems. Results. IDEFIX has been successfully tested on Intel and AMD CPUs (up to 131 072 CPU cores on Irene-Rome at TGCC) as well as NVidia and AMD GPUs (up to 1024 GPUs on Adastra at CINES). IDEFIX achieves more than 108 cell s−1 in MHD on a single NVidia V100 GPU and 3 × 1011 cell s−1 on 256 Adastra nodes (1024 GPUs) with 95% parallelization efficiency (compared to single node). For the same problem, IDEFIX is up to six times more energy efficient on GPUs compared to Intel Cascade Lake CPUs. Conclusions. IDEFIX is now a mature exascale-ready open-source code that can be used on a large variety of astrophysical and fluid dynamics applications.
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