As a special topic in computer vision, fine-grained visual categorization (FGVC) has been attracting growing atten-tion these years. Different with traditional image classifi-cation tasks in which objects have large inter-class varia-tion, the visual concepts in the fine-grained datasets

As a special topic in computer vision, fine-grained visual categorization (FGVC) has been attracting growing attention these years. Different with traditional image classification tasks in which objects have large inter-class variation, the visual concepts in the fine-grained datasets

not recognize them accurately. Traditional bag-of-words model could obtain desirable results for basic-level category classi-fication by weak alignment using spatial pyramid matching model, but may easily fail in fine-grained domains since the discriminative features are not only subtle but also extreme-ly

categorization. Salient regions, which are described by localized feature histograms, are detected as image parts. Subsequently compositions are formed as bags of parts with a locality constraint. After performing a spatial binding of compositions by means of a shape model, coupled probabilistic kernel

We investigate the fine grained object categorization problem of determining the breed of animal from an image. To this end we introduce a new annotated dataset of pets, the Oxford-IIIT-Pet dataset, covering 37 different breeds of cats and dogs. The visual problem is very challenging

of orientation and spatial frequency features. Vision research has measured the properties of these features, such as their tuning curves and sensitivity (e.g., Here, we review recent research in the domains of visual working memory and visual long-term memory, focusing on how models of these memory systems

Abstract not found

[1] There is an ongoing debate concerning the distribution of eddy stirring across the Antarctic Circumpolar Current (ACC) and the nature of its controlling processes. The problem is addressed here by estimating the isentropic eddy diffusivity from a collection of hydrographic and altimetric observations, analyzed in a mixing length theoretical framework. It is shown that, typically, is suppressed by an order of magnitude in the upper kilometer of the ACC frontal jets relative to their surroundings, primarily as a result of a local reduction of the mixing length. This observation is reproduced by a quasi‐geostrophic theory of eddy stirring across a broad barotropic jet based on the scaling law derived by Ferrari and Nikurashin (2010). The theory interprets the observed widespread suppression of the mixing length and in the upper layers of frontal jets as the kinematic consequence of eddy propagation relative to the mean flow within jet cores. Deviations from the prevalent regime of mixing suppression in the core of upper‐ocean jets are encountered in a few special sites. Such ‘leaky jet ’ segments appear to be associated with sharp stationary meanders of the mean flow that are generated by the interaction of the ACC with major topographic features. It is contended that the characteristic thermohaline structure of the Southern Ocean, consisting of multiple upper‐ocean thermohaline fronts separated and underlaid by regions of homogenized properties, is largely a result of the widespread suppression of eddy stirring by parallel jets.

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