Part Detector Discovery in Deep Convolutional Neural Networks

Current fine-grained classification approaches often rely on a robust localization of object parts to extract localized feature representations suitable for discrimination. However, part localization is a challenging task due to the large variation of appearance and pose. In this paper, we show how pre-trained convolutional neural networks can be used for robust and efficient object part discovery and localization without the necessity to actually train the network on the current dataset. Our approach called "part detector discovery" (PDD) is based on analyzing the gradient maps of the network outputs and finding activation centers spatially related to annotated semantic parts or bounding boxes. This allows us not just to obtain excellent performance on the CUB200-2011 dataset, but in contrast to previous approaches also to perform detection and bird classification jointly without requiring a given bounding box annotation during testing and ground-truth parts during training. The code is available at http://www.inf-cv.uni-jena.de/part_discovery and https://github.com/cvjena/PartDetectorDisovery.Comment: Accepted for publication on Asian Conference on Computer Vision (ACCV) 201

Deep Regionlets for Object Detection

In this paper, we propose a novel object detection framework named "Deep Regionlets" by establishing a bridge between deep neural networks and conventional detection schema for accurate generic object detection. Motivated by the abilities of regionlets for modeling object deformation and multiple aspect ratios, we incorporate regionlets into an end-to-end trainable deep learning framework. The deep regionlets framework consists of a region selection network and a deep regionlet learning module. Specifically, given a detection bounding box proposal, the region selection network provides guidance on where to select regions to learn the features from. The regionlet learning module focuses on local feature selection and transformation to alleviate local variations. To this end, we first realize non-rectangular region selection within the detection framework to accommodate variations in object appearance. Moreover, we design a "gating network" within the regionlet leaning module to enable soft regionlet selection and pooling. The Deep Regionlets framework is trained end-to-end without additional efforts. We perform ablation studies and conduct extensive experiments on the PASCAL VOC and Microsoft COCO datasets. The proposed framework outperforms state-of-the-art algorithms, such as RetinaNet and Mask R-CNN, even without additional segmentation labels.Comment: Accepted to ECCV 201

Measurement and Modeling of Wireless Off-Body Propagation Characteristics under Hospital Environment at 6-8.5 GHz

© 2013 IEEE. A measurement-based novel statistical path-loss model with a height-dependent factor and a body obstruction (BO) attenuation factor for off-body channel under a hospital environment at 6-8.5 GHz is proposed. The height-dependent factor is introduced to emulate different access point (AP) arrangement scenarios, and the BO factor is employed to describe the effect caused by different body-worn positions. The height-dependent path-loss exponent is validated to fluctuate from 2 to 4 with AP height increasing by employing both computer simulation and classical two-ray model theory. As further validated, the proposed model can provide more flexibility and higher accuracy compared with its existing counterparts. The presented channel model is expected to provide wireless link budget estimation and to further develop the physical layer algorithms for body-centric communication systems under hospital environments

Treatment of Linear and Nonlinear Dielectric Property of Molecular Monolayer and Submonolayer with Microscopic Dipole Lattice Model: I. Second Harmonic Generation and Sum-Frequency Generation

In the currently accepted models of the nonlinear optics, the nonlinear radiation was treated as the result of an infinitesimally thin polarization sheet layer, and a three layer model was generally employed. The direct consequence of this approach is that an apriori dielectric constant, which still does not have a clear definition, has to be assigned to this polarization layer. Because the Second Harmonic Generation (SHG) and the Sum-Frequency Generation vibrational Spectroscopy (SFG-VS) have been proven as the sensitive probes for interfaces with the submonolayer coverage, the treatment based on the more realistic discrete induced dipole model needs to be developed. Here we show that following the molecular optics theory approach the SHG, as well as the SFG-VS, radiation from the monolayer or submonolayer at an interface can be rigorously treated as the radiation from an induced dipole lattice at the interface. In this approach, the introduction of the polarization sheet is no longer necessary. Therefore, the ambiguity of the unaccounted dielectric constant of the polarization layer is no longer an issue. Moreover, the anisotropic two dimensional microscopic local field factors can be explicitly expressed with the linear polarizability tensors of the interfacial molecules. Based on the planewise dipole sum rule in the molecular monolayer, crucial experimental tests of this microscopic treatment with SHG and SFG-VS are discussed. Many puzzles in the literature of surface SHG and SFG spectroscopy studies can also be understood or resolved in this framework. This new treatment may provide a solid basis for the quantitative analysis in the surface SHG and SFG studies.Comment: 23 pages, 3 figure

Depth profiling of Si nanocrystals in Si-implanted SiO2 films by spectroscopic ellipsometry

An approach to determine depth profiles of silicon nanocrystals in silica films was developed. In the spectral fittings, the dielectric function of silicon nanocrystal was calculated based on two different models for the band-gap expansion due to the nanocrystal size reduction. The fitting yielded the nanocrystal depth profile and the nanocrystal size.published_or_final_versio

Revealing microstructural evolutions, mechanical properties and wear performance of wire arc additive manufacturing homogeneous and heterogeneous NiTi alloy

Heterogeneous microstructure designs have attracted a great deal of attention, not only because they have the potential to achieve an ideal combination of two conflicting properties, but also because the processes involved in their fabrication are cost-effective and can be scaled up for industrial production. The process parameters in the preparation process have an important effect on the microstructure and properties of alloy members prepared by wire arc additive manufacturing (WAAM) technology. It was expected that the spatial heterogeneous microstructure with large microstructural heterogeneities in metals can be formed through changing the process parameters. In this work, homogeneous NiTi thin-walled component and heterogeneous NiTi thin-walled component were fabricated using WAAM technology by adjusting the heat input. The effects of deposition height and heat input on the microstructure, mechanical properties and wear properties of WAAM NiTi alloys were investigated. The results show that grains were gradually refined with the increase of deposition height in the homogeneous WAAM NiTi component. The ultimate tensile strength of homogeneous WAAM NiTi component increased from 606.87 MPa to 654.45 MPa and the elongation increased from 12.72% to 15.38%, as the increase of deposition height. Moreover, the homogeneous WAAM NiTi component exhibited excellent wear resistance, the coefficient of friction decreased from 0.760 to 0.715 with the increase of deposition height. Meanwhile, the grains in the heterogeneous WAAM NiTi component shows the finest grains in the central region. The ultimate tensile strength of the lower region, middle region and upper region of heterogeneous WAAM NiTi components were 556.12 MPa, 599.53 MPa and 739.79 MPa, and the elongations were 12.98%, 16.69%, 21.74%, respectively. The coefficient of friction for the lower region, middle region and upper region of heterogeneous WAAM NiTi components were 0.713, 0.720 and 0.710, respectively. The microhardness and cyclic compression properties of the homogeneous components with higher heat input were better than those of the heterogeneous components for the same deposition height. The tensile yield strength, elongation and wear resistance of the heterogeneous components were superior compared to the homogeneous components. These results can be used to optimize the WAAM process parameters to prepare NiTi components with excellent mechanical properties

Self-organized Ge nanocrystals embedded in HfAlO fabricated by pulsed-laser deposition and application to floating gate memory

2004-2005 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Decaying Dark Matter in the Supersymmetric Standard Model with Freeze-in and Seesaw mechanims

Inspired by the decaying dark matter (DM) which can explain cosmic ray anomalies naturally, we consider the supersymmetric Standard Model with three right-handed neutrinos (RHNs) and R-parity, and introduce a TeV-scale DM sector with two fields \phi_{1,2} and a $Z_3$ discrete symmetry. The DM sector only interacts with the RHNs via a very heavy field exchange and then we can explain the cosmic ray anomalies. With the second right-handed neutrino N_2 dominant seesaw mechanism at the low scale around 10^4 GeV, we show that \phi_{1,2} can obtain the vacuum expectation values around the TeV scale, and then the lightest state from \phi_{1,2} is the decay DM with lifetime around \sim 10^{26}s. In particular, the DM very long lifetime is related to the tiny neutrino masses, and the dominant DM decay channels to \mu and \tau are related to the approximate \mu-\tau symmetry. Furthermore, the correct DM relic density can be obtained via the freeze-in mechanism, the small-scale problem for power spectrum can be solved due to the decays of the R-parity odd meta-stable states in the DM sector, and the baryon asymmetry can be generated via the soft leptogensis.Comment: 24 pages,3 figure

Controlled interfacial assembly of 2D curved colloidal crystals and jammed shells

Assembly of colloidal particles on fluid interfaces is a promising technique for synthesizing two-dimensional micro-crystalline materials useful in fields as diverse as biomedicine1, materials science2, mineral flotation3 and food processing4. Current approaches rely on bulk emulsification methods, require further chemical and thermal treatments, and are restrictive with respect to the materials employed5-9. The development of methods that exploit the great potential of interfacial assembly for producing tailored materials have been hampered by the lack of understanding of the assembly process. Here we report a microfluidic method that allows direct visualization and understanding of the dynamics of colloidal crystal growth on curved interfaces. The crystals are periodically ejected to form stable jammed shells, which we refer to as colloidal armour. We propose that the energetic barriers to interfacial crystal growth and organization can be overcome by targeted delivery of colloidal particles through hydrodynamic flows. Our method allows an unprecedented degree of control over armour composition, size and stability.Comment: 18 pages, 5 figure

Semantically Selective Augmentation for Deep Compact Person Re-Identification

We present a deep person re-identification approach that combines semantically selective, deep data augmentation with clustering-based network compression to generate high performance, light and fast inference networks. In particular, we propose to augment limited training data via sampling from a deep convolutional generative adversarial network (DCGAN), whose discriminator is constrained by a semantic classifier to explicitly control the domain specificity of the generation process. Thereby, we encode information in the classifier network which can be utilized to steer adversarial synthesis, and which fuels our CondenseNet ID-network training. We provide a quantitative and qualitative analysis of the approach and its variants on a number of datasets, obtaining results that outperform the state-of-the-art on the LIMA dataset for long-term monitoring in indoor living spaces