Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification

Person re-identification (re-id) aims to match pedestrians observed by disjoint camera views. It attracts increasing attention in computer vision due to its importance to surveillance system. To combat the major challenge of cross-view visual variations, deep embedding approaches are proposed by learning a compact feature space from images such that the Euclidean distances correspond to their cross-view similarity metric. However, the global Euclidean distance cannot faithfully characterize the ideal similarity in a complex visual feature space because features of pedestrian images exhibit unknown distributions due to large variations in poses, illumination and occlusion. Moreover, intra-personal training samples within a local range are robust to guide deep embedding against uncontrolled variations, which however, cannot be captured by a global Euclidean distance. In this paper, we study the problem of person re-id by proposing a novel sampling to mine suitable \textit{positives} (i.e. intra-class) within a local range to improve the deep embedding in the context of large intra-class variations. Our method is capable of learning a deep similarity metric adaptive to local sample structure by minimizing each sample's local distances while propagating through the relationship between samples to attain the whole intra-class minimization. To this end, a novel objective function is proposed to jointly optimize similarity metric learning, local positive mining and robust deep embedding. This yields local discriminations by selecting local-ranged positive samples, and the learned features are robust to dramatic intra-class variations. Experiments on benchmarks show state-of-the-art results achieved by our method.Comment: Published on Pattern Recognitio

Multi-modal gated recurrent units for image description

Using a natural language sentence to describe the content of an image is a challenging but very important task. It is challenging because a description must not only capture objects contained in the image and the relationships among them, but also be relevant and grammatically correct. In this paper a multi-modal embedding model based on gated recurrent units (GRU) which can generate variable-length description for a given image. In the training step, we apply the convolutional neural network (CNN) to extract the image feature. Then the feature is imported into the multi-modal GRU as well as the corresponding sentence representations. The multi-modal GRU learns the inter-modal relations between image and sentence. And in the testing step, when an image is imported to our multi-modal GRU model, a sentence which describes the image content is generated. The experimental results demonstrate that our multi-modal GRU model obtains the state-of-the-art performance on Flickr8K, Flickr30K and MS COCO datasets.Comment: 25 pages, 7 figures, 6 tables, magazin

A complex storm system in Saturn’s north polar atmosphere in 2018

Producción CientíficaSaturn’s convective storms usually fall in two categories. One consists of mid-sized storms ∼2,000 km wide, appearing as irregular bright cloud systems that evolve rapidly, on scales of a few days. The other includes the Great White Spots, planetary-scale giant storms ten times larger than the mid-sized ones, which disturb a full latitude band, enduring several months, and have been observed only seven times since 1876. Here we report a new intermediate type, observed in 2018 in the north polar region. Four large storms with east–west lengths ∼4,000–8,000 km (the first one lasting longer than 200 days) formed sequentially in close latitudes, experiencing mutual encounters and leading to zonal disturbances affecting a full latitude band ∼8,000 km wide, during at least eight months. Dynamical simulations indicate that each storm required energies around ten times larger than mid-sized storms but ∼100 times smaller than those necessary for a Great White Spot. This event occurred at about the same latitude and season as the Great White Spot in 1960, in close correspondence with the cycle of approximately 60 years hypothesized for equatorial Great White Spots.Ministerio de Economía, Industria y Competitividad - Fondo Europeo de Desarrollo Regional (project AYA2015-65041-P)Gobierno Vasco (project IT-366-19

Computational Depth-resolved Imaging and Metrology

In this thesis, the main research challenge boils down to extracting 3D spatial information of an object from 2D measurements using light. Our goal is to achieve depth-resolved tomographic imaging of transparent or semi-transparent 3D objects, and to perform topography characterization of rough surfaces. The essential tool we used is computational imaging, where depending on the experimental scheme, often indirect measurements are taken, and tailored algorithms are employed to perform image reconstructions. The computational imaging approach enables us to relax the hardware requirement of an imaging system, which is essential when using light in the EUV and x-ray regimes, where high-quality optics are not readily available. In this thesis, visible and infrared light sources are used, where computational imaging also offers several advantages. First of all, it often leads to a simple, flexible imaging system with low cost. In the case of a lensless configuration, where no lenses are involved in the final image-forming stage between the object and the detector, aberration-free image reconstructions can be obtained. More importantly, computational imaging provides quantitative reconstructions of scalar electric fields, enabling phase imaging, numerical refocus, as well as 3D imaging