Efficient Action Detection in Untrimmed Videos via Multi-Task Learning

This paper studies the joint learning of action recognition and temporal localization in long, untrimmed videos. We employ a multi-task learning framework that performs the three highly related steps of action proposal, action recognition, and action localization refinement in parallel instead of the standard sequential pipeline that performs the steps in order. We develop a novel temporal actionness regression module that estimates what proportion of a clip contains action. We use it for temporal localization but it could have other applications like video retrieval, surveillance, summarization, etc. We also introduce random shear augmentation during training to simulate viewpoint change. We evaluate our framework on three popular video benchmarks. Results demonstrate that our joint model is efficient in terms of storage and computation in that we do not need to compute and cache dense trajectory features, and that it is several times faster than its sequential ConvNets counterpart. Yet, despite being more efficient, it outperforms state-of-the-art methods with respect to accuracy.Comment: WACV 2017 camera ready, minor updates about test time efficienc

Microscopic description of neutron emission rates in compound nuclei

The neutron emission rates in thermal excited nuclei are conventionally described by statistical models with a phenomenological level density parameter that depends on excitation energies, deformations and mass regions. In the microscopic view of hot nuclei, the neutron emission rates can be determined by the external neutron gas densities without any free parameters. Therefore the microscopic description of thermal neutron emissions is desirable that can impact several understandings such as survival probabilities of superheavy compound nuclei and neutron emissivity in reactors. To describe the neutron emission rates microscopically, the external thermal neutron gases are self-consistently obtained based on the Finite-Temperature Hartree-Fock-Bogoliubov (FT-HFB) approach. The results are compared with the statistical model to explore the connections between the FT-HFB approach and the statistical model. The Skyrme FT-HFB equation is solved by HFB-AX in deformed coordinate spaces. Based on the FT-HFB approach, the thermal properties and external neutron gas are properly described with the self-consistent gas substraction procedure. Then neutron emission rates can be obtained based on the densities of external neutron gases. The thermal statistical properties of $^{238}$U and $^{258}$U are studied in detail in terms of excitation energies. The thermal neutron emission rates in $^{238, 258}$U and superheavy compound nuclei $_{112}^{278}$Cn and $_{114}^{292}$Fl are calculated, which agree well with the statistical model by adopting an excitation-energy-dependent level density parameter. The coordinate-space FT-HFB approach can provide reliable microscopic descriptions of neutron emission rates in hot nuclei, as well as microscopic constraints on the excitation energy dependence of level density parameters for statistical models.Comment: 6 pages, 5 figures, revised and accepted for PR