Dense Feature Aggregation and Pruning for RGBT Tracking

How to perform effective information fusion of different modalities is a core factor in boosting the performance of RGBT tracking. This paper presents a novel deep fusion algorithm based on the representations from an end-to-end trained convolutional neural network. To deploy the complementarity of features of all layers, we propose a recursive strategy to densely aggregate these features that yield robust representations of target objects in each modality. In different modalities, we propose to prune the densely aggregated features of all modalities in a collaborative way. In a specific, we employ the operations of global average pooling and weighted random selection to perform channel scoring and selection, which could remove redundant and noisy features to achieve more robust feature representation. Experimental results on two RGBT tracking benchmark datasets suggest that our tracker achieves clear state-of-the-art against other RGB and RGBT tracking methods.Comment: arXiv admin note: text overlap with arXiv:1811.0985

On the Optimization of Advanced DCF-Trackers

Trackers based on discriminative correlation filters (DCF) have recently seen widespread success and in this work we dive into their numerical core. DCF-based trackers interleave learning of the target detector and target state inference based on this detector. Whereas the original formulation includes a closed-form solution for the filter learning, recently introduced improvements to the framework no longer have known closed-form solutions. Instead a large-scale linear least squares problem must be solved each time the detector is updated. We analyze the procedure used to optimize the detector and let the popular scheme introduced with ECO serve as a baseline. The ECO implementation is revisited in detail and several mechanisms are provided with alternatives. With comprehensive experiments we show which configurations are superior in terms of tracking capabilities and optimization performance.Funding agencies: Swedish Foundation for Strategic Research (SymbiCloud); Swedish Research Council (EMC 2)Swedish Research Council [201605543]; CENIIT grant [18.14]; Swedish National Infrastructure for Computing; Wallenberg AI, Autonomous Systems and Software Program (WASP</p

A geometry for sub-nanosecond X-ray diffraction from laser-shocked polycrystalline foils

In situ picosecond X-ray diffraction has proved to be a useful tool in furthering our understanding of the response of shocked crystals at the lattice level. To date the vast majority of this work has used single crystals as the shocked samples, owing to their diffraction efficiency, although the study of the response of polycrystalline samples is clearly of interest for many applications. We present here the results of experiments to develop sub-nanosecond powder/polycrystalline diffraction using a cylindrical pinhole camera. By allowing the incident X-ray beam to impinge on the sample at non-normal angles, the response of grains making a variety of angles to the shock propagation direction can potentially be interrogated. © 2007 American Institute of Physics

Carbohydrates and resistance to Phytophthora infestans in potato plants

peer reviewedPlants generally deal with biotic or abiotic stresses by altering components as for example cell wall constituents and metabolites. Infection by Phytophthora infestans, the causal agent of late blight, constitutes a stress condition for the plants and they react to it with changes arising in their metabolism depending on the resistance level of the plants. The present work compares two potato hybrids differing in their level of horizontal resistance to late blight. Carbohydrate content in stems and leaves of infected and uninfected plants was determined by HPLC. Some carbohydrates accumulated in the stems of the resistant hybrid infected by P. infestans, whereas they remained unchanged in the susceptible hybrid. On the other hand, in the leaves, these carbohydrates accumulated only in the infected susceptible hybrid